Overview

Dataset statistics

Number of variables71
Number of observations457994
Missing cells6098857
Missing cells (%)18.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory248.1 MiB
Average record size in memory568.0 B

Variable types

Numeric17
Categorical54

Alerts

Recording Fixation filter name has constant value "Tobii I-VT (Fixation)"Constant
Recording software version has constant value "1.145.28180"Constant
Recording resolution height has constant value "1080"Constant
Recording resolution width has constant value "1920"Constant
Recording monitor latency has constant value "10,00"Constant
Gaze direction left X has a high cardinality: 61420 distinct valuesHigh cardinality
Gaze direction left Y has a high cardinality: 55109 distinct valuesHigh cardinality
Gaze direction left Z has a high cardinality: 11729 distinct valuesHigh cardinality
Gaze direction right X has a high cardinality: 60429 distinct valuesHigh cardinality
Gaze direction right Y has a high cardinality: 53969 distinct valuesHigh cardinality
Gaze direction right Z has a high cardinality: 11241 distinct valuesHigh cardinality
Pupil diameter left has a high cardinality: 471 distinct valuesHigh cardinality
Pupil diameter right has a high cardinality: 509 distinct valuesHigh cardinality
Eye position left X (DACSmm) has a high cardinality: 1742 distinct valuesHigh cardinality
Eye position left Y (DACSmm) has a high cardinality: 2573 distinct valuesHigh cardinality
Eye position left Z (DACSmm) has a high cardinality: 4834 distinct valuesHigh cardinality
Eye position right X (DACSmm) has a high cardinality: 1808 distinct valuesHigh cardinality
Eye position right Y (DACSmm) has a high cardinality: 2662 distinct valuesHigh cardinality
Eye position right Z (DACSmm) has a high cardinality: 4955 distinct valuesHigh cardinality
Gaze point left X (DACSmm) has a high cardinality: 5688 distinct valuesHigh cardinality
Gaze point left Y (DACSmm) has a high cardinality: 3918 distinct valuesHigh cardinality
Gaze point right X (DACSmm) has a high cardinality: 5773 distinct valuesHigh cardinality
Gaze point right Y (DACSmm) has a high cardinality: 3978 distinct valuesHigh cardinality
Gaze point X (MCSnorm) has a high cardinality: 9841 distinct valuesHigh cardinality
Gaze point Y (MCSnorm) has a high cardinality: 9924 distinct valuesHigh cardinality
Gaze point left X (MCSnorm) has a high cardinality: 9875 distinct valuesHigh cardinality
Gaze point left Y (MCSnorm) has a high cardinality: 9896 distinct valuesHigh cardinality
Gaze point right X (MCSnorm) has a high cardinality: 9596 distinct valuesHigh cardinality
Gaze point right Y (MCSnorm) has a high cardinality: 9848 distinct valuesHigh cardinality
Fixation point X (MCSnorm) has a high cardinality: 3601 distinct valuesHigh cardinality
Fixation point Y (MCSnorm) has a high cardinality: 3830 distinct valuesHigh cardinality
Sensor is highly imbalanced (90.0%)Imbalance
Presented Media height is highly imbalanced (54.1%)Imbalance
Presented Media position Y (DACSpx) is highly imbalanced (54.1%)Imbalance
Eyetracker timestamp has 6707 (1.5%) missing valuesMissing
Event has 457231 (99.8%) missing valuesMissing
Event value has 457290 (99.8%) missing valuesMissing
Gaze point X has 59258 (12.9%) missing valuesMissing
Gaze point Y has 59258 (12.9%) missing valuesMissing
Gaze point left X has 82053 (17.9%) missing valuesMissing
Gaze point left Y has 82053 (17.9%) missing valuesMissing
Gaze point right X has 117001 (25.5%) missing valuesMissing
Gaze point right Y has 117001 (25.5%) missing valuesMissing
Gaze direction left X has 82053 (17.9%) missing valuesMissing
Gaze direction left Y has 82053 (17.9%) missing valuesMissing
Gaze direction left Z has 82053 (17.9%) missing valuesMissing
Gaze direction right X has 117001 (25.5%) missing valuesMissing
Gaze direction right Y has 117001 (25.5%) missing valuesMissing
Gaze direction right Z has 117001 (25.5%) missing valuesMissing
Pupil diameter left has 336313 (73.4%) missing valuesMissing
Pupil diameter right has 348819 (76.2%) missing valuesMissing
Validity left has 6707 (1.5%) missing valuesMissing
Validity right has 6707 (1.5%) missing valuesMissing
Eye position left X (DACSmm) has 82053 (17.9%) missing valuesMissing
Eye position left Y (DACSmm) has 82053 (17.9%) missing valuesMissing
Eye position left Z (DACSmm) has 82053 (17.9%) missing valuesMissing
Eye position right X (DACSmm) has 117001 (25.5%) missing valuesMissing
Eye position right Y (DACSmm) has 117001 (25.5%) missing valuesMissing
Eye position right Z (DACSmm) has 117001 (25.5%) missing valuesMissing
Gaze point left X (DACSmm) has 82053 (17.9%) missing valuesMissing
Gaze point left Y (DACSmm) has 82053 (17.9%) missing valuesMissing
Gaze point right X (DACSmm) has 117001 (25.5%) missing valuesMissing
Gaze point right Y (DACSmm) has 117001 (25.5%) missing valuesMissing
Gaze point X (MCSnorm) has 82366 (18.0%) missing valuesMissing
Gaze point Y (MCSnorm) has 82366 (18.0%) missing valuesMissing
Gaze point left X (MCSnorm) has 102522 (22.4%) missing valuesMissing
Gaze point left Y (MCSnorm) has 102522 (22.4%) missing valuesMissing
Gaze point right X (MCSnorm) has 141115 (30.8%) missing valuesMissing
Gaze point right Y (MCSnorm) has 141115 (30.8%) missing valuesMissing
Fixation point X has 193902 (42.3%) missing valuesMissing
Fixation point Y has 193902 (42.3%) missing valuesMissing
Fixation point X (MCSnorm) has 208868 (45.6%) missing valuesMissing
Fixation point Y (MCSnorm) has 208868 (45.6%) missing valuesMissing
Mouse position X has 452050 (98.7%) missing valuesMissing
Mouse position Y has 452050 (98.7%) missing valuesMissing

Reproduction

Analysis started2023-04-23 19:22:49.711263
Analysis finished2023-04-23 19:24:46.389168
Duration1 minute and 56.68 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Distinct126034
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48390.064
Minimum8112
Maximum150536
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-04-23T20:24:46.502662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum8112
5-th percentile11071
Q119359
median33076
Q372484
95-th percentile124549.35
Maximum150536
Range142424
Interquartile range (IQR)53125

Descriptive statistics

Standard deviation36888.41
Coefficient of variation (CV)0.76231373
Kurtosis-0.10765581
Mean48390.064
Median Absolute Deviation (MAD)18314
Skewness0.99372609
Sum2.2162359 × 1010
Variance1.3607548 × 109
MonotonicityNot monotonic
2023-04-23T20:24:46.630985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17179 16
 
< 0.1%
17311 16
 
< 0.1%
17318 16
 
< 0.1%
17317 16
 
< 0.1%
17316 16
 
< 0.1%
17315 16
 
< 0.1%
17314 16
 
< 0.1%
17313 16
 
< 0.1%
17312 16
 
< 0.1%
17310 16
 
< 0.1%
Other values (126024) 457834
> 99.9%
ValueCountFrequency (%)
8112 1
< 0.1%
8113 1
< 0.1%
8114 1
< 0.1%
8115 1
< 0.1%
8116 1
< 0.1%
8117 1
< 0.1%
8118 1
< 0.1%
8119 1
< 0.1%
8120 1
< 0.1%
8121 1
< 0.1%
ValueCountFrequency (%)
150536 1
< 0.1%
150535 1
< 0.1%
150534 1
< 0.1%
150533 1
< 0.1%
150532 1
< 0.1%
150531 1
< 0.1%
150530 1
< 0.1%
150529 1
< 0.1%
150528 1
< 0.1%
150527 1
< 0.1%

Recording timestamp
Real number (ℝ)

Distinct285053
Distinct (%)62.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56776335
Minimum2414233
Maximum2.2828593 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-04-23T20:24:46.778666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2414233
5-th percentile9996081.4
Q125369000
median44123392
Q363554647
95-th percentile1.8115097 × 108
Maximum2.2828593 × 108
Range2.2587169 × 108
Interquartile range (IQR)38185647

Descriptive statistics

Standard deviation48698073
Coefficient of variation (CV)0.8577178
Kurtosis2.7742196
Mean56776335
Median Absolute Deviation (MAD)19082634
Skewness1.808049
Sum2.6003221 × 1013
Variance2.3715023 × 1015
MonotonicityNot monotonic
2023-04-23T20:24:46.912474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6735083 8
 
< 0.1%
11998313 8
 
< 0.1%
10820483 8
 
< 0.1%
7780190 8
 
< 0.1%
10901545 8
 
< 0.1%
168015080 8
 
< 0.1%
64628793 8
 
< 0.1%
11894950 8
 
< 0.1%
13079623 8
 
< 0.1%
64534447 8
 
< 0.1%
Other values (285043) 457914
> 99.9%
ValueCountFrequency (%)
2414233 2
< 0.1%
2420379 1
< 0.1%
2428710 1
< 0.1%
2437018 1
< 0.1%
2445345 1
< 0.1%
2453727 1
< 0.1%
2462015 1
< 0.1%
2470373 1
< 0.1%
2478677 1
< 0.1%
2487012 1
< 0.1%
ValueCountFrequency (%)
228285927 4
< 0.1%
228277557 4
< 0.1%
228269225 4
< 0.1%
228260894 4
< 0.1%
228252561 4
< 0.1%
228244229 4
< 0.1%
228235897 4
< 0.1%
228227565 4
< 0.1%
228225047 4
< 0.1%
228219229 4
< 0.1%

Computer timestamp
Real number (ℝ)

Distinct285870
Distinct (%)62.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6747719 × 1011
Minimum2.0975106 × 109
Maximum4.0591706 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-04-23T20:24:47.051810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.0975106 × 109
5-th percentile2.9118898 × 109
Q14.3518343 × 1011
median5.1557956 × 1011
Q35.2067637 × 1011
95-th percentile3.3772931 × 1012
Maximum4.0591706 × 1012
Range4.0570731 × 1012
Interquartile range (IQR)8.5492944 × 1010

Descriptive statistics

Standard deviation8.5442536 × 1011
Coefficient of variation (CV)1.2800817
Kurtosis8.0805601
Mean6.6747719 × 1011
Median Absolute Deviation (MAD)8.0216143 × 1010
Skewness2.9483154
Sum3.0570055 × 1017
Variance7.300427 × 1023
MonotonicityNot monotonic
2023-04-23T20:24:47.201336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.151099038 × 10118
 
< 0.1%
5.151150868 × 10118
 
< 0.1%
5.151891353 × 10118
 
< 0.1%
5.151140703 × 10118
 
< 0.1%
5.151849521 × 10118
 
< 0.1%
5.155665638 × 10118
 
< 0.1%
5.155664694 × 10118
 
< 0.1%
5.1556639 × 10118
 
< 0.1%
5.155097152 × 10118
 
< 0.1%
5.156699501 × 10118
 
< 0.1%
Other values (285860) 457914
> 99.9%
ValueCountFrequency (%)
2097510607 2
< 0.1%
2097512049 1
< 0.1%
2097520289 1
< 0.1%
2097521891 1
< 0.1%
2097528627 1
< 0.1%
2097536939 1
< 0.1%
2097545305 1
< 0.1%
2097553649 1
< 0.1%
2097562019 1
< 0.1%
2097570286 1
< 0.1%
ValueCountFrequency (%)
4.059170622 × 10121
< 0.1%
4.059170606 × 10121
< 0.1%
4.059170597 × 10121
< 0.1%
4.059170593 × 10122
< 0.1%
4.059170589 × 10121
< 0.1%
4.059170581 × 10121
< 0.1%
4.059170572 × 10121
< 0.1%
4.059170564 × 10121
< 0.1%
4.059170556 × 10121
< 0.1%
4.059170547 × 10121
< 0.1%

Sensor
Categorical

Distinct2
Distinct (%)< 0.1%
Missing763
Missing (%)0.2%
Memory size3.5 MiB
Eye Tracker
451287 
Mouse
 
5944

Length

Max length11
Median length11
Mean length10.922
Min length5

Characters and Unicode

Total characters4993877
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEye Tracker
2nd rowEye Tracker
3rd rowEye Tracker
4th rowEye Tracker
5th rowEye Tracker

Common Values

ValueCountFrequency (%)
Eye Tracker 451287
98.5%
Mouse 5944
 
1.3%
(Missing) 763
 
0.2%

Length

2023-04-23T20:24:47.332139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T20:24:47.457745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
eye 451287
49.7%
tracker 451287
49.7%
mouse 5944
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e 908518
18.2%
r 902574
18.1%
E 451287
9.0%
y 451287
9.0%
451287
9.0%
T 451287
9.0%
a 451287
9.0%
c 451287
9.0%
k 451287
9.0%
M 5944
 
0.1%
Other values (3) 17832
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3634072
72.8%
Uppercase Letter 908518
 
18.2%
Space Separator 451287
 
9.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 908518
25.0%
r 902574
24.8%
y 451287
12.4%
a 451287
12.4%
c 451287
12.4%
k 451287
12.4%
o 5944
 
0.2%
u 5944
 
0.2%
s 5944
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
E 451287
49.7%
T 451287
49.7%
M 5944
 
0.7%
Space Separator
ValueCountFrequency (%)
451287
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4542590
91.0%
Common 451287
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 908518
20.0%
r 902574
19.9%
E 451287
9.9%
y 451287
9.9%
T 451287
9.9%
a 451287
9.9%
c 451287
9.9%
k 451287
9.9%
M 5944
 
0.1%
o 5944
 
0.1%
Other values (2) 11888
 
0.3%
Common
ValueCountFrequency (%)
451287
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4993877
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 908518
18.2%
r 902574
18.1%
E 451287
9.0%
y 451287
9.0%
451287
9.0%
T 451287
9.0%
a 451287
9.0%
c 451287
9.0%
k 451287
9.0%
M 5944
 
0.1%
Other values (3) 17832
 
0.4%

Project name
Categorical

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
Control group experiment
287863 
Participant0018
 
22821
Participant10
 
20651
Participånt0014
 
9605
Participant0044
 
9184
Other values (15)
107870 

Length

Max length24
Median length24
Mean length20.582355
Min length13

Characters and Unicode

Total characters9426595
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowControl group experiment
2nd rowControl group experiment
3rd rowControl group experiment
4th rowControl group experiment
5th rowControl group experiment

Common Values

ValueCountFrequency (%)
Control group experiment 287863
62.9%
Participant0018 22821
 
5.0%
Participant10 20651
 
4.5%
Participånt0014 9605
 
2.1%
Participant0044 9184
 
2.0%
Participant0036 8004
 
1.7%
Participant0026 7770
 
1.7%
Participant0030 7764
 
1.7%
Participant0050 7755
 
1.7%
Participant0028 7365
 
1.6%
Other values (10) 69212
 
15.1%

Length

2023-04-23T20:24:47.600000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
control 287863
27.8%
group 287863
27.8%
experiment 287863
27.8%
participant0018 22821
 
2.2%
participant10 20651
 
2.0%
participånt0014 9605
 
0.9%
participant0044 9184
 
0.9%
participant0036 8004
 
0.8%
participant0026 7770
 
0.8%
participant0030 7764
 
0.8%
Other values (12) 84332
 
8.2%

Most occurring characters

ValueCountFrequency (%)
r 1033720
11.0%
t 915988
 
9.7%
o 863589
 
9.2%
e 863589
 
9.2%
n 745857
 
7.9%
p 745857
 
7.9%
i 628125
 
6.7%
575726
 
6.1%
0 341368
 
3.6%
a 330657
 
3.5%
Other values (17) 2382119
25.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7753653
82.3%
Decimal Number 639222
 
6.8%
Space Separator 575726
 
6.1%
Uppercase Letter 457994
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1033720
13.3%
t 915988
11.8%
o 863589
11.1%
e 863589
11.1%
n 745857
9.6%
p 745857
9.6%
i 628125
8.1%
a 330657
 
4.3%
m 287863
 
3.7%
x 287863
 
3.7%
Other values (6) 1050545
13.5%
Decimal Number
ValueCountFrequency (%)
0 341368
53.4%
4 68231
 
10.7%
1 53077
 
8.3%
8 51794
 
8.1%
5 36644
 
5.7%
3 30332
 
4.7%
6 28928
 
4.5%
2 28848
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
C 287863
62.9%
P 170131
37.1%
Space Separator
ValueCountFrequency (%)
575726
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8211647
87.1%
Common 1214948
 
12.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1033720
12.6%
t 915988
11.2%
o 863589
10.5%
e 863589
10.5%
n 745857
9.1%
p 745857
9.1%
i 628125
7.6%
a 330657
 
4.0%
C 287863
 
3.5%
m 287863
 
3.5%
Other values (8) 1508539
18.4%
Common
ValueCountFrequency (%)
575726
47.4%
0 341368
28.1%
4 68231
 
5.6%
1 53077
 
4.4%
8 51794
 
4.3%
5 36644
 
3.0%
3 30332
 
2.5%
6 28928
 
2.4%
2 28848
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9416990
99.9%
None 9605
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1033720
11.0%
t 915988
 
9.7%
o 863589
 
9.2%
e 863589
 
9.2%
n 745857
 
7.9%
p 745857
 
7.9%
i 628125
 
6.7%
575726
 
6.1%
0 341368
 
3.6%
a 330657
 
3.5%
Other values (16) 2372514
25.2%
None
ValueCountFrequency (%)
Ã¥ 9605
100.0%

Export date
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
20.10.2020
115892 
07.10.2020
86952 
06.10.2020
56718 
30.09.2020
28301 
05.02.2021
22821 
Other values (11)
147310 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters4579940
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30.09.2020
2nd row30.09.2020
3rd row30.09.2020
4th row30.09.2020
5th row30.09.2020

Common Values

ValueCountFrequency (%)
20.10.2020 115892
25.3%
07.10.2020 86952
19.0%
06.10.2020 56718
12.4%
30.09.2020 28301
 
6.2%
05.02.2021 22821
 
5.0%
08.09.2021 21678
 
4.7%
29.10.2020 20651
 
4.5%
16.03.2021 20007
 
4.4%
13.03.2021 15283
 
3.3%
17.02.2021 15135
 
3.3%
Other values (6) 54556
11.9%

Length

2023-04-23T20:24:47.706205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20.10.2020 115892
25.3%
07.10.2020 86952
19.0%
06.10.2020 56718
12.4%
30.09.2020 28301
 
6.2%
05.02.2021 22821
 
5.0%
08.09.2021 21678
 
4.7%
29.10.2020 20651
 
4.5%
16.03.2021 20007
 
4.4%
13.03.2021 15283
 
3.3%
17.02.2021 15135
 
3.3%
Other values (6) 54556
11.9%

Most occurring characters

ValueCountFrequency (%)
0 1556864
34.0%
2 1122723
24.5%
. 915988
20.0%
1 519807
 
11.3%
7 109298
 
2.4%
6 91834
 
2.0%
3 85986
 
1.9%
9 77841
 
1.7%
8 59418
 
1.3%
5 32426
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3663952
80.0%
Other Punctuation 915988
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1556864
42.5%
2 1122723
30.6%
1 519807
 
14.2%
7 109298
 
3.0%
6 91834
 
2.5%
3 85986
 
2.3%
9 77841
 
2.1%
8 59418
 
1.6%
5 32426
 
0.9%
4 7755
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 915988
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4579940
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1556864
34.0%
2 1122723
24.5%
. 915988
20.0%
1 519807
 
11.3%
7 109298
 
2.4%
6 91834
 
2.0%
3 85986
 
1.9%
9 77841
 
1.7%
8 59418
 
1.3%
5 32426
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4579940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1556864
34.0%
2 1122723
24.5%
. 915988
20.0%
1 519807
 
11.3%
7 109298
 
2.4%
6 91834
 
2.0%
3 85986
 
1.9%
9 77841
 
1.7%
8 59418
 
1.3%
5 32426
 
0.7%

Participant name
Categorical

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
Participant0002
113204 
Participant0004
85251 
Participant0006
60468 
Participant0008
28940 
Participant0018
22821 
Other values (18)
147310 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters6869910
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParticipant0002
2nd rowParticipant0002
3rd rowParticipant0002
4th rowParticipant0002
5th rowParticipant0002

Common Values

ValueCountFrequency (%)
Participant0002 113204
24.7%
Participant0004 85251
18.6%
Participant0006 60468
13.2%
Participant0008 28940
 
6.3%
Participant0018 22821
 
5.0%
Participant0010 20651
 
4.5%
Participant0014 9605
 
2.1%
Participant0044 9184
 
2.0%
Participant0036 8004
 
1.7%
Participant0026 7770
 
1.7%
Other values (13) 92096
20.1%

Length

2023-04-23T20:24:47.803836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
participant0002 113204
24.7%
participant0004 85251
18.6%
participant0006 60468
13.2%
participant0008 28940
 
6.3%
participant0018 22821
 
5.0%
participant0010 20651
 
4.5%
participant0014 9605
 
2.1%
participant0044 9184
 
2.0%
participant0036 8004
 
1.7%
participant0026 7770
 
1.7%
Other values (13) 92096
20.1%

Most occurring characters

ValueCountFrequency (%)
0 1246259
18.1%
a 915988
13.3%
t 915988
13.3%
i 915988
13.3%
P 457994
 
6.7%
r 457994
 
6.7%
c 457994
 
6.7%
p 457994
 
6.7%
n 457994
 
6.7%
4 153482
 
2.2%
Other values (6) 432235
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4579940
66.7%
Decimal Number 1831976
 
26.7%
Uppercase Letter 457994
 
6.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1246259
68.0%
4 153482
 
8.4%
2 142052
 
7.8%
6 89396
 
4.9%
8 80734
 
4.4%
1 53077
 
2.9%
5 36644
 
2.0%
3 30332
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
a 915988
20.0%
t 915988
20.0%
i 915988
20.0%
r 457994
10.0%
c 457994
10.0%
p 457994
10.0%
n 457994
10.0%
Uppercase Letter
ValueCountFrequency (%)
P 457994
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5037934
73.3%
Common 1831976
 
26.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1246259
68.0%
4 153482
 
8.4%
2 142052
 
7.8%
6 89396
 
4.9%
8 80734
 
4.4%
1 53077
 
2.9%
5 36644
 
2.0%
3 30332
 
1.7%
Latin
ValueCountFrequency (%)
a 915988
18.2%
t 915988
18.2%
i 915988
18.2%
P 457994
9.1%
r 457994
9.1%
c 457994
9.1%
p 457994
9.1%
n 457994
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6869910
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1246259
18.1%
a 915988
13.3%
t 915988
13.3%
i 915988
13.3%
P 457994
 
6.7%
r 457994
 
6.7%
c 457994
 
6.7%
p 457994
 
6.7%
n 457994
 
6.7%
4 153482
 
2.2%
Other values (6) 432235
 
6.3%

Recording name
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
Recording5
107084 
Recording2
82018 
Recording3
80849 
Recording8
24072 
Recording9
22536 
Other values (11)
141435 

Length

Max length11
Median length10
Mean length10.195217
Min length10

Characters and Unicode

Total characters4669348
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRecording2
2nd rowRecording2
3rd rowRecording2
4th rowRecording2
5th rowRecording2

Common Values

ValueCountFrequency (%)
Recording5 107084
23.4%
Recording2 82018
17.9%
Recording3 80849
17.7%
Recording8 24072
 
5.3%
Recording9 22536
 
4.9%
Recording7 21897
 
4.8%
Recording6 16746
 
3.7%
Recording12 15514
 
3.4%
Recording11 15298
 
3.3%
Recording10 14892
 
3.3%
Other values (6) 57088
12.5%

Length

2023-04-23T20:24:47.907871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
recording5 107084
23.4%
recording2 82018
17.9%
recording3 80849
17.7%
recording8 24072
 
5.3%
recording9 22536
 
4.9%
recording7 21897
 
4.8%
recording6 16746
 
3.7%
recording12 15514
 
3.4%
recording11 15298
 
3.3%
recording10 14892
 
3.3%
Other values (6) 57088
12.5%

Most occurring characters

ValueCountFrequency (%)
R 457994
9.8%
e 457994
9.8%
c 457994
9.8%
o 457994
9.8%
r 457994
9.8%
d 457994
9.8%
i 457994
9.8%
n 457994
9.8%
g 457994
9.8%
5 114327
 
2.4%
Other values (9) 433075
9.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3663952
78.5%
Decimal Number 547402
 
11.7%
Uppercase Letter 457994
 
9.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 114327
20.9%
1 104706
19.1%
2 97532
17.8%
3 95613
17.5%
7 29129
 
5.3%
8 24072
 
4.4%
6 23987
 
4.4%
9 22536
 
4.1%
4 20608
 
3.8%
0 14892
 
2.7%
Lowercase Letter
ValueCountFrequency (%)
e 457994
12.5%
c 457994
12.5%
o 457994
12.5%
r 457994
12.5%
d 457994
12.5%
i 457994
12.5%
n 457994
12.5%
g 457994
12.5%
Uppercase Letter
ValueCountFrequency (%)
R 457994
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4121946
88.3%
Common 547402
 
11.7%

Most frequent character per script

Common
ValueCountFrequency (%)
5 114327
20.9%
1 104706
19.1%
2 97532
17.8%
3 95613
17.5%
7 29129
 
5.3%
8 24072
 
4.4%
6 23987
 
4.4%
9 22536
 
4.1%
4 20608
 
3.8%
0 14892
 
2.7%
Latin
ValueCountFrequency (%)
R 457994
11.1%
e 457994
11.1%
c 457994
11.1%
o 457994
11.1%
r 457994
11.1%
d 457994
11.1%
i 457994
11.1%
n 457994
11.1%
g 457994
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4669348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 457994
9.8%
e 457994
9.8%
c 457994
9.8%
o 457994
9.8%
r 457994
9.8%
d 457994
9.8%
i 457994
9.8%
n 457994
9.8%
g 457994
9.8%
5 114327
 
2.4%
Other values (9) 433075
9.3%

Recording date
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
30.09.2020
113204 
06.10.2020
85251 
07.10.2020
60468 
20.10.2020
28940 
05.02.2021
22821 
Other values (11)
147310 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters4579940
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30.09.2020
2nd row30.09.2020
3rd row30.09.2020
4th row30.09.2020
5th row30.09.2020

Common Values

ValueCountFrequency (%)
30.09.2020 113204
24.7%
06.10.2020 85251
18.6%
07.10.2020 60468
13.2%
20.10.2020 28940
 
6.3%
05.02.2021 22821
 
5.0%
08.09.2021 21678
 
4.7%
29.10.2020 20651
 
4.5%
16.03.2021 20007
 
4.4%
13.03.2021 15283
 
3.3%
17.02.2021 15135
 
3.3%
Other values (6) 54556
11.9%

Length

2023-04-23T20:24:48.048163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
30.09.2020 113204
24.7%
06.10.2020 85251
18.6%
07.10.2020 60468
13.2%
20.10.2020 28940
 
6.3%
05.02.2021 22821
 
5.0%
08.09.2021 21678
 
4.7%
29.10.2020 20651
 
4.5%
16.03.2021 20007
 
4.4%
13.03.2021 15283
 
3.3%
17.02.2021 15135
 
3.3%
Other values (6) 54556
11.9%

Most occurring characters

ValueCountFrequency (%)
0 1556864
34.0%
2 1035771
22.6%
. 915988
20.0%
1 434904
 
9.5%
3 170889
 
3.7%
9 162744
 
3.6%
6 120367
 
2.6%
7 82814
 
1.8%
8 59418
 
1.3%
5 32426
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3663952
80.0%
Other Punctuation 915988
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1556864
42.5%
2 1035771
28.3%
1 434904
 
11.9%
3 170889
 
4.7%
9 162744
 
4.4%
6 120367
 
3.3%
7 82814
 
2.3%
8 59418
 
1.6%
5 32426
 
0.9%
4 7755
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 915988
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4579940
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1556864
34.0%
2 1035771
22.6%
. 915988
20.0%
1 434904
 
9.5%
3 170889
 
3.7%
9 162744
 
3.6%
6 120367
 
2.6%
7 82814
 
1.8%
8 59418
 
1.3%
5 32426
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4579940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1556864
34.0%
2 1035771
22.6%
. 915988
20.0%
1 434904
 
9.5%
3 170889
 
3.7%
9 162744
 
3.6%
6 120367
 
2.6%
7 82814
 
1.8%
8 59418
 
1.3%
5 32426
 
0.7%
Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
30.09.2020
113204 
06.10.2020
85251 
07.10.2020
60468 
20.10.2020
28940 
05.02.2021
22821 
Other values (11)
147310 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters4579940
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30.09.2020
2nd row30.09.2020
3rd row30.09.2020
4th row30.09.2020
5th row30.09.2020

Common Values

ValueCountFrequency (%)
30.09.2020 113204
24.7%
06.10.2020 85251
18.6%
07.10.2020 60468
13.2%
20.10.2020 28940
 
6.3%
05.02.2021 22821
 
5.0%
08.09.2021 21678
 
4.7%
29.10.2020 20651
 
4.5%
16.03.2021 20007
 
4.4%
13.03.2021 15283
 
3.3%
17.02.2021 15135
 
3.3%
Other values (6) 54556
11.9%

Length

2023-04-23T20:24:48.145359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
30.09.2020 113204
24.7%
06.10.2020 85251
18.6%
07.10.2020 60468
13.2%
20.10.2020 28940
 
6.3%
05.02.2021 22821
 
5.0%
08.09.2021 21678
 
4.7%
29.10.2020 20651
 
4.5%
16.03.2021 20007
 
4.4%
13.03.2021 15283
 
3.3%
17.02.2021 15135
 
3.3%
Other values (6) 54556
11.9%

Most occurring characters

ValueCountFrequency (%)
0 1556864
34.0%
2 1035771
22.6%
. 915988
20.0%
1 434904
 
9.5%
3 170889
 
3.7%
9 162744
 
3.6%
6 120367
 
2.6%
7 82814
 
1.8%
8 59418
 
1.3%
5 32426
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3663952
80.0%
Other Punctuation 915988
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1556864
42.5%
2 1035771
28.3%
1 434904
 
11.9%
3 170889
 
4.7%
9 162744
 
4.4%
6 120367
 
3.3%
7 82814
 
2.3%
8 59418
 
1.6%
5 32426
 
0.9%
4 7755
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 915988
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4579940
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1556864
34.0%
2 1035771
22.6%
. 915988
20.0%
1 434904
 
9.5%
3 170889
 
3.7%
9 162744
 
3.6%
6 120367
 
2.6%
7 82814
 
1.8%
8 59418
 
1.3%
5 32426
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4579940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1556864
34.0%
2 1035771
22.6%
. 915988
20.0%
1 434904
 
9.5%
3 170889
 
3.7%
9 162744
 
3.6%
6 120367
 
2.6%
7 82814
 
1.8%
8 59418
 
1.3%
5 32426
 
0.7%
Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
14:59:55.440
107084 
16:25:57.570
 
24072
16:27:52.233
 
22536
16:24:30.647
 
21897
16:22:30.483
 
16746
Other values (33)
265659 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters5495928
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row14:53:16.980
2nd row14:53:16.980
3rd row14:53:16.980
4th row14:53:16.980
5th row14:53:16.980

Common Values

ValueCountFrequency (%)
14:59:55.440 107084
23.4%
16:25:57.570 24072
 
5.3%
16:27:52.233 22536
 
4.9%
16:24:30.647 21897
 
4.8%
16:22:30.483 16746
 
3.7%
16:07:23.510 15514
 
3.4%
16:05:59.467 15298
 
3.3%
16:04:05.503 14892
 
3.3%
16:09:00.157 14764
 
3.2%
15:54:30.620 9605
 
2.1%
Other values (28) 195586
42.7%

Length

2023-04-23T20:24:48.240750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
14:59:55.440 107084
23.4%
16:25:57.570 24072
 
5.3%
16:27:52.233 22536
 
4.9%
16:24:30.647 21897
 
4.8%
16:22:30.483 16746
 
3.7%
16:07:23.510 15514
 
3.4%
16:05:59.467 15298
 
3.3%
16:04:05.503 14892
 
3.3%
16:09:00.157 14764
 
3.2%
15:54:30.620 9605
 
2.1%
Other values (28) 195586
42.7%

Most occurring characters

ValueCountFrequency (%)
: 915988
16.7%
4 678023
12.3%
1 658428
12.0%
5 645358
11.7%
0 495211
9.0%
. 457994
8.3%
3 375364
6.8%
7 335584
 
6.1%
2 309935
 
5.6%
6 287516
 
5.2%
Other values (2) 336527
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4121946
75.0%
Other Punctuation 1373982
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 678023
16.4%
1 658428
16.0%
5 645358
15.7%
0 495211
12.0%
3 375364
9.1%
7 335584
8.1%
2 309935
7.5%
6 287516
7.0%
9 247703
 
6.0%
8 88824
 
2.2%
Other Punctuation
ValueCountFrequency (%)
: 915988
66.7%
. 457994
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 5495928
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 915988
16.7%
4 678023
12.3%
1 658428
12.0%
5 645358
11.7%
0 495211
9.0%
. 457994
8.3%
3 375364
6.8%
7 335584
 
6.1%
2 309935
 
5.6%
6 287516
 
5.2%
Other values (2) 336527
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5495928
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 915988
16.7%
4 678023
12.3%
1 658428
12.0%
5 645358
11.7%
0 495211
9.0%
. 457994
8.3%
3 375364
6.8%
7 335584
 
6.1%
2 309935
 
5.6%
6 287516
 
5.2%
Other values (2) 336527
 
6.1%
Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
12:59:55.440
107084 
14:25:57.570
 
24072
14:27:52.233
 
22536
14:24:30.647
 
21897
14:22:30.483
 
16746
Other values (33)
265659 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters5495928
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12:53:16.980
2nd row12:53:16.980
3rd row12:53:16.980
4th row12:53:16.980
5th row12:53:16.980

Common Values

ValueCountFrequency (%)
12:59:55.440 107084
23.4%
14:25:57.570 24072
 
5.3%
14:27:52.233 22536
 
4.9%
14:24:30.647 21897
 
4.8%
14:22:30.483 16746
 
3.7%
14:07:23.510 15514
 
3.4%
14:05:59.467 15298
 
3.3%
14:04:05.503 14892
 
3.3%
14:09:00.157 14764
 
3.2%
14:54:30.620 9605
 
2.1%
Other values (28) 195586
42.7%

Length

2023-04-23T20:24:48.337093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12:59:55.440 107084
23.4%
14:25:57.570 24072
 
5.3%
14:27:52.233 22536
 
4.9%
14:24:30.647 21897
 
4.8%
14:22:30.483 16746
 
3.7%
14:07:23.510 15514
 
3.4%
14:05:59.467 15298
 
3.3%
14:04:05.503 14892
 
3.3%
14:09:00.157 14764
 
3.2%
14:54:30.620 9605
 
2.1%
Other values (28) 195586
42.7%

Most occurring characters

ValueCountFrequency (%)
: 915988
16.7%
4 669901
12.2%
1 630284
11.5%
5 589899
10.7%
0 509543
9.3%
2 458755
8.3%
. 457994
8.3%
3 426250
7.8%
7 335584
 
6.1%
9 269805
 
4.9%
Other values (2) 231925
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4121946
75.0%
Other Punctuation 1373982
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 669901
16.3%
1 630284
15.3%
5 589899
14.3%
0 509543
12.4%
2 458755
11.1%
3 426250
10.3%
7 335584
8.1%
9 269805
6.5%
6 135872
 
3.3%
8 96053
 
2.3%
Other Punctuation
ValueCountFrequency (%)
: 915988
66.7%
. 457994
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 5495928
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 915988
16.7%
4 669901
12.2%
1 630284
11.5%
5 589899
10.7%
0 509543
9.3%
2 458755
8.3%
. 457994
8.3%
3 426250
7.8%
7 335584
 
6.1%
9 269805
 
4.9%
Other values (2) 231925
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5495928
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 915988
16.7%
4 669901
12.2%
1 630284
11.5%
5 589899
10.7%
0 509543
9.3%
2 458755
8.3%
. 457994
8.3%
3 426250
7.8%
7 335584
 
6.1%
9 269805
 
4.9%
Other values (2) 231925
 
4.2%

Recording duration
Real number (ℝ)

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105622.61
Minimum14124
Maximum228445
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-04-23T20:24:48.648405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum14124
5-th percentile55214
Q166107
median70463
Q386171
95-th percentile228445
Maximum228445
Range214321
Interquartile range (IQR)20064

Descriptive statistics

Standard deviation68399.952
Coefficient of variation (CV)0.64758813
Kurtosis-0.45281972
Mean105622.61
Median Absolute Deviation (MAD)6535
Skewness1.1953338
Sum4.8374524 × 1010
Variance4.6785534 × 109
MonotonicityNot monotonic
2023-04-23T20:24:48.769472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
228445 107084
23.4%
80204 24072
 
5.3%
66829 22536
 
4.9%
69592 21897
 
4.8%
70463 16746
 
3.7%
70839 15514
 
3.4%
70868 15298
 
3.3%
73324 14892
 
3.3%
79029 14764
 
3.2%
86171 9605
 
2.1%
Other values (28) 195586
42.7%
ValueCountFrequency (%)
14124 3564
0.8%
15290 2556
 
0.6%
52541 5925
1.3%
52697 6100
1.3%
55214 6238
1.4%
57085 6484
1.4%
62841 7112
1.6%
63645 7279
1.6%
63827 7243
1.6%
63857 7211
1.6%
ValueCountFrequency (%)
228445 107084
23.4%
86171 9605
 
2.1%
80431 9184
 
2.0%
80204 24072
 
5.3%
79029 14764
 
3.2%
74005 7232
 
1.6%
73324 14892
 
3.3%
70868 15298
 
3.3%
70839 15514
 
3.4%
70463 16746
 
3.7%

Timeline name
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
Timeline1
365845 
Timeline2
92149 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters4121946
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTimeline1
2nd rowTimeline1
3rd rowTimeline1
4th rowTimeline1
5th rowTimeline1

Common Values

ValueCountFrequency (%)
Timeline1 365845
79.9%
Timeline2 92149
 
20.1%

Length

2023-04-23T20:24:48.893148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T20:24:48.998408image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
timeline1 365845
79.9%
timeline2 92149
 
20.1%

Most occurring characters

ValueCountFrequency (%)
i 915988
22.2%
e 915988
22.2%
T 457994
11.1%
m 457994
11.1%
l 457994
11.1%
n 457994
11.1%
1 365845
 
8.9%
2 92149
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3205958
77.8%
Uppercase Letter 457994
 
11.1%
Decimal Number 457994
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 915988
28.6%
e 915988
28.6%
m 457994
14.3%
l 457994
14.3%
n 457994
14.3%
Decimal Number
ValueCountFrequency (%)
1 365845
79.9%
2 92149
 
20.1%
Uppercase Letter
ValueCountFrequency (%)
T 457994
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3663952
88.9%
Common 457994
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 915988
25.0%
e 915988
25.0%
T 457994
12.5%
m 457994
12.5%
l 457994
12.5%
n 457994
12.5%
Common
ValueCountFrequency (%)
1 365845
79.9%
2 92149
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4121946
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 915988
22.2%
e 915988
22.2%
T 457994
11.1%
m 457994
11.1%
l 457994
11.1%
n 457994
11.1%
1 365845
 
8.9%
2 92149
 
2.2%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
Tobii I-VT (Fixation)
457994 

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters9617874
Distinct characters15
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTobii I-VT (Fixation)
2nd rowTobii I-VT (Fixation)
3rd rowTobii I-VT (Fixation)
4th rowTobii I-VT (Fixation)
5th rowTobii I-VT (Fixation)

Common Values

ValueCountFrequency (%)
Tobii I-VT (Fixation) 457994
100.0%

Length

2023-04-23T20:24:49.090441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T20:24:49.195435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
tobii 457994
33.3%
i-vt 457994
33.3%
fixation 457994
33.3%

Most occurring characters

ValueCountFrequency (%)
i 1831976
19.0%
T 915988
 
9.5%
o 915988
 
9.5%
915988
 
9.5%
b 457994
 
4.8%
I 457994
 
4.8%
- 457994
 
4.8%
V 457994
 
4.8%
( 457994
 
4.8%
F 457994
 
4.8%
Other values (5) 2289970
23.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5037934
52.4%
Uppercase Letter 2289970
23.8%
Space Separator 915988
 
9.5%
Dash Punctuation 457994
 
4.8%
Open Punctuation 457994
 
4.8%
Close Punctuation 457994
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 1831976
36.4%
o 915988
18.2%
b 457994
 
9.1%
x 457994
 
9.1%
a 457994
 
9.1%
t 457994
 
9.1%
n 457994
 
9.1%
Uppercase Letter
ValueCountFrequency (%)
T 915988
40.0%
I 457994
20.0%
V 457994
20.0%
F 457994
20.0%
Space Separator
ValueCountFrequency (%)
915988
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 457994
100.0%
Open Punctuation
ValueCountFrequency (%)
( 457994
100.0%
Close Punctuation
ValueCountFrequency (%)
) 457994
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7327904
76.2%
Common 2289970
 
23.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 1831976
25.0%
T 915988
12.5%
o 915988
12.5%
b 457994
 
6.2%
I 457994
 
6.2%
V 457994
 
6.2%
F 457994
 
6.2%
x 457994
 
6.2%
a 457994
 
6.2%
t 457994
 
6.2%
Common
ValueCountFrequency (%)
915988
40.0%
- 457994
20.0%
( 457994
20.0%
) 457994
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9617874
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 1831976
19.0%
T 915988
 
9.5%
o 915988
 
9.5%
915988
 
9.5%
b 457994
 
4.8%
I 457994
 
4.8%
- 457994
 
4.8%
V 457994
 
4.8%
( 457994
 
4.8%
F 457994
 
4.8%
Other values (5) 2289970
23.8%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
1.145.28180
457994 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters5037934
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.145.28180
2nd row1.145.28180
3rd row1.145.28180
4th row1.145.28180
5th row1.145.28180

Common Values

ValueCountFrequency (%)
1.145.28180 457994
100.0%

Length

2023-04-23T20:24:49.275047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T20:24:49.370096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.145.28180 457994
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1373982
27.3%
. 915988
18.2%
8 915988
18.2%
4 457994
 
9.1%
5 457994
 
9.1%
2 457994
 
9.1%
0 457994
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4121946
81.8%
Other Punctuation 915988
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1373982
33.3%
8 915988
22.2%
4 457994
 
11.1%
5 457994
 
11.1%
2 457994
 
11.1%
0 457994
 
11.1%
Other Punctuation
ValueCountFrequency (%)
. 915988
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5037934
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1373982
27.3%
. 915988
18.2%
8 915988
18.2%
4 457994
 
9.1%
5 457994
 
9.1%
2 457994
 
9.1%
0 457994
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5037934
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1373982
27.3%
. 915988
18.2%
8 915988
18.2%
4 457994
 
9.1%
5 457994
 
9.1%
2 457994
 
9.1%
0 457994
 
9.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
1080
457994 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1831976
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1080
2nd row1080
3rd row1080
4th row1080
5th row1080

Common Values

ValueCountFrequency (%)
1080 457994
100.0%

Length

2023-04-23T20:24:49.449404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T20:24:49.543296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1080 457994
100.0%

Most occurring characters

ValueCountFrequency (%)
0 915988
50.0%
1 457994
25.0%
8 457994
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1831976
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 915988
50.0%
1 457994
25.0%
8 457994
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1831976
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 915988
50.0%
1 457994
25.0%
8 457994
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1831976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 915988
50.0%
1 457994
25.0%
8 457994
25.0%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
1920
457994 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1831976
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1920
2nd row1920
3rd row1920
4th row1920
5th row1920

Common Values

ValueCountFrequency (%)
1920 457994
100.0%

Length

2023-04-23T20:24:49.617295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T20:24:49.712107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1920 457994
100.0%

Most occurring characters

ValueCountFrequency (%)
1 457994
25.0%
9 457994
25.0%
2 457994
25.0%
0 457994
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1831976
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 457994
25.0%
9 457994
25.0%
2 457994
25.0%
0 457994
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1831976
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 457994
25.0%
9 457994
25.0%
2 457994
25.0%
0 457994
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1831976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 457994
25.0%
9 457994
25.0%
2 457994
25.0%
0 457994
25.0%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
10,00
457994 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters2289970
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10,00
2nd row10,00
3rd row10,00
4th row10,00
5th row10,00

Common Values

ValueCountFrequency (%)
10,00 457994
100.0%

Length

2023-04-23T20:24:49.788477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T20:24:49.884776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
10,00 457994
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1373982
60.0%
1 457994
 
20.0%
, 457994
 
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1831976
80.0%
Other Punctuation 457994
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1373982
75.0%
1 457994
 
25.0%
Other Punctuation
ValueCountFrequency (%)
, 457994
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2289970
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1373982
60.0%
1 457994
 
20.0%
, 457994
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2289970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1373982
60.0%
1 457994
 
20.0%
, 457994
 
20.0%

Eyetracker timestamp
Real number (ℝ)

Distinct281813
Distinct (%)62.4%
Missing6707
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean4.321977 × 109
Minimum3.8289255 × 108
Maximum7.8836664 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-04-23T20:24:49.985438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.8289255 × 108
5-th percentile4.8250078 × 108
Q11.1319595 × 109
median2.6192315 × 109
Q33.0817563 × 109
95-th percentile9.8029816 × 109
Maximum7.8836664 × 1010
Range7.8453772 × 1010
Interquartile range (IQR)1.9497968 × 109

Descriptive statistics

Standard deviation1.0075901 × 1010
Coefficient of variation (CV)2.3313177
Kurtosis47.0119
Mean4.321977 × 109
Median Absolute Deviation (MAD)1.4673321 × 109
Skewness6.7631504
Sum1.950452 × 1015
Variance1.0152379 × 1020
MonotonicityNot monotonic
2023-04-23T20:24:50.128123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1146620277 4
 
< 0.1%
1146712014 4
 
< 0.1%
1146703593 4
 
< 0.1%
1146695261 4
 
< 0.1%
1146686931 4
 
< 0.1%
1146678597 4
 
< 0.1%
1146670264 4
 
< 0.1%
1146661932 4
 
< 0.1%
1146653659 4
 
< 0.1%
1146645322 4
 
< 0.1%
Other values (281803) 451247
98.5%
(Missing) 6707
 
1.5%
ValueCountFrequency (%)
382892548 1
< 0.1%
382900909 1
< 0.1%
382909245 1
< 0.1%
382917557 1
< 0.1%
382925890 1
< 0.1%
382934229 1
< 0.1%
382942545 1
< 0.1%
382950877 1
< 0.1%
382959212 1
< 0.1%
382967538 1
< 0.1%
ValueCountFrequency (%)
7.883666422 × 10101
< 0.1%
7.883665593 × 10101
< 0.1%
7.883664759 × 10101
< 0.1%
7.883663926 × 10101
< 0.1%
7.883663093 × 10101
< 0.1%
7.883662256 × 10101
< 0.1%
7.883661425 × 10101
< 0.1%
7.883660591 × 10101
< 0.1%
7.883659761 × 10101
< 0.1%
7.883658925 × 10101
< 0.1%

Event
Categorical

Distinct5
Distinct (%)0.7%
Missing457231
Missing (%)99.8%
Memory size3.5 MiB
KeyboardEvent
268 
MouseEvent
168 
ImageStimulusStart
134 
ImageStimulusEnd
134 
Eye tracker Calibration end
59 

Length

Max length27
Median length18
Mean length14.826999
Min length10

Characters and Unicode

Total characters11313
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEye tracker Calibration end
2nd rowImageStimulusStart
3rd rowMouseEvent
4th rowMouseEvent
5th rowImageStimulusEnd

Common Values

ValueCountFrequency (%)
KeyboardEvent 268
 
0.1%
MouseEvent 168
 
< 0.1%
ImageStimulusStart 134
 
< 0.1%
ImageStimulusEnd 134
 
< 0.1%
Eye tracker Calibration end 59
 
< 0.1%
(Missing) 457231
99.8%

Length

2023-04-23T20:24:50.274076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T20:24:50.407253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
keyboardevent 268
28.5%
mouseevent 168
17.9%
imagestimulusstart 134
14.3%
imagestimulusend 134
14.3%
eye 59
 
6.3%
tracker 59
 
6.3%
calibration 59
 
6.3%
end 59
 
6.3%

Most occurring characters

ValueCountFrequency (%)
e 1317
 
11.6%
t 1090
 
9.6%
a 847
 
7.5%
u 704
 
6.2%
n 688
 
6.1%
E 629
 
5.6%
r 579
 
5.1%
m 536
 
4.7%
o 495
 
4.4%
d 461
 
4.1%
Other values (15) 3967
35.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9342
82.6%
Uppercase Letter 1794
 
15.9%
Space Separator 177
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1317
14.1%
t 1090
11.7%
a 847
 
9.1%
u 704
 
7.5%
n 688
 
7.4%
r 579
 
6.2%
m 536
 
5.7%
o 495
 
5.3%
d 461
 
4.9%
s 436
 
4.7%
Other values (8) 2189
23.4%
Uppercase Letter
ValueCountFrequency (%)
E 629
35.1%
S 402
22.4%
I 268
14.9%
K 268
14.9%
M 168
 
9.4%
C 59
 
3.3%
Space Separator
ValueCountFrequency (%)
177
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11136
98.4%
Common 177
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1317
 
11.8%
t 1090
 
9.8%
a 847
 
7.6%
u 704
 
6.3%
n 688
 
6.2%
E 629
 
5.6%
r 579
 
5.2%
m 536
 
4.8%
o 495
 
4.4%
d 461
 
4.1%
Other values (14) 3790
34.0%
Common
ValueCountFrequency (%)
177
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11313
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1317
 
11.6%
t 1090
 
9.6%
a 847
 
7.5%
u 704
 
6.2%
n 688
 
6.1%
E 629
 
5.6%
r 579
 
5.1%
m 536
 
4.7%
o 495
 
4.4%
d 461
 
4.1%
Other values (15) 3967
35.1%

Event value
Categorical

Distinct11
Distinct (%)1.6%
Missing457290
Missing (%)99.8%
Memory size3.5 MiB
[Ctrl] + LeftControl
264 
babelia 6164137243739591
118 
Down, Left
84 
Up, Left
84 
Photo1
42 
Other values (6)
112 

Length

Max length24
Median length20
Mean length15.215909
Min length6

Characters and Unicode

Total characters10712
Distinct characters33
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbabelia 6164137243739591
2nd rowDown, Left
3rd rowUp, Left
4th rowbabelia 6164137243739591
5th rowPhoto1

Common Values

ValueCountFrequency (%)
[Ctrl] + LeftControl 264
 
0.1%
babelia 6164137243739591 118
 
< 0.1%
Down, Left 84
 
< 0.1%
Up, Left 84
 
< 0.1%
Photo1 42
 
< 0.1%
Photo2 36
 
< 0.1%
Photo3 36
 
< 0.1%
Photo1 (1) 16
 
< 0.1%
Photo2 (1) 12
 
< 0.1%
Photo3 (1) 8
 
< 0.1%
(Missing) 457290
99.8%

Length

2023-04-23T20:24:50.537008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
leftcontrol 268
17.2%
ctrl 264
17.0%
264
17.0%
left 168
10.8%
babelia 118
7.6%
6164137243739591 118
7.6%
down 84
 
5.4%
up 84
 
5.4%
photo1 58
 
3.7%
photo2 48
 
3.1%
Other values (2) 80
 
5.1%

Most occurring characters

ValueCountFrequency (%)
t 1118
 
10.4%
o 920
 
8.6%
850
 
7.9%
l 650
 
6.1%
e 554
 
5.2%
r 532
 
5.0%
C 532
 
5.0%
1 448
 
4.2%
L 436
 
4.1%
f 436
 
4.1%
Other values (23) 4236
39.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5470
51.1%
Decimal Number 2074
 
19.4%
Uppercase Letter 1286
 
12.0%
Space Separator 850
 
7.9%
Close Punctuation 300
 
2.8%
Open Punctuation 300
 
2.8%
Math Symbol 264
 
2.5%
Other Punctuation 168
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1118
20.4%
o 920
16.8%
l 650
11.9%
e 554
10.1%
r 532
9.7%
f 436
 
8.0%
n 352
 
6.4%
a 236
 
4.3%
b 236
 
4.3%
h 150
 
2.7%
Other values (3) 286
 
5.2%
Decimal Number
ValueCountFrequency (%)
1 448
21.6%
3 398
19.2%
4 236
11.4%
9 236
11.4%
7 236
11.4%
6 236
11.4%
2 166
 
8.0%
5 118
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
C 532
41.4%
L 436
33.9%
P 150
 
11.7%
D 84
 
6.5%
U 84
 
6.5%
Close Punctuation
ValueCountFrequency (%)
] 264
88.0%
) 36
 
12.0%
Open Punctuation
ValueCountFrequency (%)
[ 264
88.0%
( 36
 
12.0%
Space Separator
ValueCountFrequency (%)
850
100.0%
Math Symbol
ValueCountFrequency (%)
+ 264
100.0%
Other Punctuation
ValueCountFrequency (%)
, 168
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6756
63.1%
Common 3956
36.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1118
16.5%
o 920
13.6%
l 650
9.6%
e 554
8.2%
r 532
7.9%
C 532
7.9%
L 436
 
6.5%
f 436
 
6.5%
n 352
 
5.2%
a 236
 
3.5%
Other values (8) 990
14.7%
Common
ValueCountFrequency (%)
850
21.5%
1 448
11.3%
3 398
10.1%
+ 264
 
6.7%
] 264
 
6.7%
[ 264
 
6.7%
4 236
 
6.0%
9 236
 
6.0%
7 236
 
6.0%
6 236
 
6.0%
Other values (5) 524
13.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10712
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1118
 
10.4%
o 920
 
8.6%
850
 
7.9%
l 650
 
6.1%
e 554
 
5.2%
r 532
 
5.0%
C 532
 
5.0%
1 448
 
4.2%
L 436
 
4.1%
f 436
 
4.1%
Other values (23) 4236
39.5%

Gaze point X
Real number (ℝ)

Distinct2232
Distinct (%)0.6%
Missing59258
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean934.96542
Minimum-351
Maximum2296
Zeros10
Zeros (%)< 0.1%
Negative440
Negative (%)0.1%
Memory size3.5 MiB
2023-04-23T20:24:50.663477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-351
5-th percentile325
Q1748
median943
Q31115
95-th percentile1524
Maximum2296
Range2647
Interquartile range (IQR)367

Descriptive statistics

Standard deviation340.08993
Coefficient of variation (CV)0.363746
Kurtosis0.463428
Mean934.96542
Median Absolute Deviation (MAD)182
Skewness0.058310409
Sum3.7280437 × 108
Variance115661.16
MonotonicityNot monotonic
2023-04-23T20:24:50.799049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
970 823
 
0.2%
971 813
 
0.2%
969 811
 
0.2%
972 791
 
0.2%
925 774
 
0.2%
997 774
 
0.2%
1003 772
 
0.2%
975 771
 
0.2%
887 769
 
0.2%
974 766
 
0.2%
Other values (2222) 390872
85.3%
(Missing) 59258
 
12.9%
ValueCountFrequency (%)
-351 1
 
< 0.1%
-110 2
 
< 0.1%
-82 2
 
< 0.1%
-75 4
< 0.1%
-67 8
< 0.1%
-65 4
< 0.1%
-64 4
< 0.1%
-63 4
< 0.1%
-58 4
< 0.1%
-56 1
 
< 0.1%
ValueCountFrequency (%)
2296 4
< 0.1%
2293 1
 
< 0.1%
2290 1
 
< 0.1%
2285 2
< 0.1%
2283 1
 
< 0.1%
2277 1
 
< 0.1%
2276 1
 
< 0.1%
2275 1
 
< 0.1%
2257 1
 
< 0.1%
2252 2
< 0.1%

Gaze point Y
Real number (ℝ)

Distinct1488
Distinct (%)0.4%
Missing59258
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean405.50068
Minimum-214
Maximum1911
Zeros163
Zeros (%)< 0.1%
Negative18931
Negative (%)4.1%
Memory size3.5 MiB
2023-04-23T20:24:50.937499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-214
5-th percentile5
Q1232
median377
Q3573
95-th percentile874
Maximum1911
Range2125
Interquartile range (IQR)341

Descriptive statistics

Standard deviation255.93906
Coefficient of variation (CV)0.63116802
Kurtosis-0.18953617
Mean405.50068
Median Absolute Deviation (MAD)168
Skewness0.33757102
Sum1.6168772 × 108
Variance65504.804
MonotonicityNot monotonic
2023-04-23T20:24:51.068647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
359 799
 
0.2%
304 792
 
0.2%
305 789
 
0.2%
284 777
 
0.2%
303 774
 
0.2%
290 774
 
0.2%
300 770
 
0.2%
362 769
 
0.2%
310 765
 
0.2%
363 761
 
0.2%
Other values (1478) 390966
85.4%
(Missing) 59258
 
12.9%
ValueCountFrequency (%)
-214 3
 
< 0.1%
-213 8
 
< 0.1%
-211 5
 
< 0.1%
-210 3
 
< 0.1%
-209 4
 
< 0.1%
-208 14
 
< 0.1%
-207 7
 
< 0.1%
-206 3
 
< 0.1%
-205 38
< 0.1%
-204 12
 
< 0.1%
ValueCountFrequency (%)
1911 1
 
< 0.1%
1892 1
 
< 0.1%
1877 2
< 0.1%
1794 1
 
< 0.1%
1540 3
< 0.1%
1500 1
 
< 0.1%
1289 3
< 0.1%
1281 1
 
< 0.1%
1278 3
< 0.1%
1277 4
< 0.1%

Gaze point left X
Real number (ℝ)

Distinct2181
Distinct (%)0.6%
Missing82053
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean872.62093
Minimum-351
Maximum2296
Zeros25
Zeros (%)< 0.1%
Negative1472
Negative (%)0.3%
Memory size3.5 MiB
2023-04-23T20:24:51.201054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-351
5-th percentile273
Q1686
median874
Q31052
95-th percentile1468
Maximum2296
Range2647
Interquartile range (IQR)366

Descriptive statistics

Standard deviation339.6415
Coefficient of variation (CV)0.38921998
Kurtosis0.3980915
Mean872.62093
Median Absolute Deviation (MAD)183
Skewness0.074179448
Sum3.2805399 × 108
Variance115356.35
MonotonicityNot monotonic
2023-04-23T20:24:51.337576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
856 971
 
0.2%
858 931
 
0.2%
853 928
 
0.2%
855 905
 
0.2%
862 885
 
0.2%
857 870
 
0.2%
854 852
 
0.2%
852 846
 
0.2%
859 843
 
0.2%
851 831
 
0.2%
Other values (2171) 367079
80.1%
(Missing) 82053
 
17.9%
ValueCountFrequency (%)
-351 1
 
< 0.1%
-125 4
< 0.1%
-122 4
< 0.1%
-116 2
 
< 0.1%
-115 6
< 0.1%
-112 4
< 0.1%
-110 4
< 0.1%
-106 8
< 0.1%
-105 6
< 0.1%
-100 1
 
< 0.1%
ValueCountFrequency (%)
2296 4
< 0.1%
2229 1
 
< 0.1%
2212 1
 
< 0.1%
2211 1
 
< 0.1%
2209 1
 
< 0.1%
2197 2
< 0.1%
2178 1
 
< 0.1%
2164 1
 
< 0.1%
2153 1
 
< 0.1%
2141 2
< 0.1%

Gaze point left Y
Real number (ℝ)

Distinct1458
Distinct (%)0.4%
Missing82053
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean416.04835
Minimum-214
Maximum1911
Zeros166
Zeros (%)< 0.1%
Negative15712
Negative (%)3.4%
Memory size3.5 MiB
2023-04-23T20:24:51.477545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-214
5-th percentile20
Q1241
median385
Q3582
95-th percentile884
Maximum1911
Range2125
Interquartile range (IQR)341

Descriptive statistics

Standard deviation254.96279
Coefficient of variation (CV)0.6128201
Kurtosis-0.20422268
Mean416.04835
Median Absolute Deviation (MAD)167
Skewness0.32929427
Sum1.5640963 × 108
Variance65006.024
MonotonicityNot monotonic
2023-04-23T20:24:51.607179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
317 827
 
0.2%
332 802
 
0.2%
318 778
 
0.2%
327 776
 
0.2%
322 775
 
0.2%
312 773
 
0.2%
333 763
 
0.2%
313 762
 
0.2%
319 762
 
0.2%
328 759
 
0.2%
Other values (1448) 368164
80.4%
(Missing) 82053
 
17.9%
ValueCountFrequency (%)
-214 11
 
< 0.1%
-213 6
 
< 0.1%
-212 4
 
< 0.1%
-211 7
 
< 0.1%
-209 3
 
< 0.1%
-208 7
 
< 0.1%
-207 42
< 0.1%
-206 6
 
< 0.1%
-205 7
 
< 0.1%
-204 6
 
< 0.1%
ValueCountFrequency (%)
1911 1
< 0.1%
1892 1
< 0.1%
1877 2
< 0.1%
1794 1
< 0.1%
1500 1
< 0.1%
1256 1
< 0.1%
1253 1
< 0.1%
1251 2
< 0.1%
1249 1
< 0.1%
1248 1
< 0.1%

Gaze point right X
Real number (ℝ)

Distinct2256
Distinct (%)0.7%
Missing117001
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean1006.5645
Minimum-29
Maximum2304
Zeros8
Zeros (%)< 0.1%
Negative12
Negative (%)< 0.1%
Memory size3.5 MiB
2023-04-23T20:24:51.743901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-29
5-th percentile389
Q1818
median1005
Q31198
95-th percentile1594
Maximum2304
Range2333
Interquartile range (IQR)380

Descriptive statistics

Standard deviation344.82609
Coefficient of variation (CV)0.34257725
Kurtosis0.53673686
Mean1006.5645
Median Absolute Deviation (MAD)190
Skewness0.11563311
Sum3.4323144 × 108
Variance118905.03
MonotonicityNot monotonic
2023-04-23T20:24:51.873674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
930 651
 
0.1%
920 651
 
0.1%
919 650
 
0.1%
936 648
 
0.1%
921 627
 
0.1%
945 624
 
0.1%
941 609
 
0.1%
922 606
 
0.1%
929 605
 
0.1%
928 601
 
0.1%
Other values (2246) 334721
73.1%
(Missing) 117001
 
25.5%
ValueCountFrequency (%)
-29 4
< 0.1%
-25 4
< 0.1%
-3 4
< 0.1%
0 8
< 0.1%
1 4
< 0.1%
7 4
< 0.1%
8 4
< 0.1%
11 4
< 0.1%
14 4
< 0.1%
15 4
< 0.1%
ValueCountFrequency (%)
2304 12
< 0.1%
2303 6
 
< 0.1%
2300 2
 
< 0.1%
2294 4
 
< 0.1%
2293 1
 
< 0.1%
2292 8
< 0.1%
2291 12
< 0.1%
2290 17
< 0.1%
2288 4
 
< 0.1%
2287 2
 
< 0.1%

Gaze point right Y
Real number (ℝ)

Distinct1498
Distinct (%)0.4%
Missing117001
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean387.43858
Minimum-216
Maximum1540
Zeros176
Zeros (%)< 0.1%
Negative18891
Negative (%)4.1%
Memory size3.5 MiB
2023-04-23T20:24:52.006454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-216
5-th percentile-12
Q1213
median365
Q3551
95-th percentile855
Maximum1540
Range1756
Interquartile range (IQR)338

Descriptive statistics

Standard deviation255.06658
Coefficient of variation (CV)0.65834068
Kurtosis-0.080134977
Mean387.43858
Median Absolute Deviation (MAD)167
Skewness0.36320568
Sum1.3211384 × 108
Variance65058.96
MonotonicityNot monotonic
2023-04-23T20:24:52.137643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
290 755
 
0.2%
287 733
 
0.2%
331 723
 
0.2%
288 720
 
0.2%
302 717
 
0.2%
298 712
 
0.2%
297 711
 
0.2%
321 699
 
0.2%
291 696
 
0.2%
289 695
 
0.2%
Other values (1488) 333832
72.9%
(Missing) 117001
 
25.5%
ValueCountFrequency (%)
-216 3
 
< 0.1%
-214 2
 
< 0.1%
-213 15
< 0.1%
-212 8
 
< 0.1%
-211 8
 
< 0.1%
-210 10
 
< 0.1%
-209 2
 
< 0.1%
-208 13
 
< 0.1%
-207 14
< 0.1%
-206 34
< 0.1%
ValueCountFrequency (%)
1540 3
 
< 0.1%
1317 1
 
< 0.1%
1308 1
 
< 0.1%
1307 3
 
< 0.1%
1305 1
 
< 0.1%
1304 1
 
< 0.1%
1301 3
 
< 0.1%
1299 1
 
< 0.1%
1296 2
 
< 0.1%
1295 8
< 0.1%

Gaze direction left X
Categorical

HIGH CARDINALITY  MISSING 

Distinct61420
Distinct (%)16.3%
Missing82053
Missing (%)17.9%
Memory size3.5 MiB
0,04964
 
45
0,05145
 
43
0,03738
 
43
0,02665
 
42
0,05123
 
42
Other values (61415)
375726 

Length

Max length8
Median length7
Mean length7.3351377
Min length7

Characters and Unicode

Total characters2757579
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12694 ?
Unique (%)3.4%

Sample

1st row0,04662
2nd row0,04769
3rd row0,05295
4th row0,04921
5th row0,04838

Common Values

ValueCountFrequency (%)
0,04964 45
 
< 0.1%
0,05145 43
 
< 0.1%
0,03738 43
 
< 0.1%
0,02665 42
 
< 0.1%
0,05123 42
 
< 0.1%
0,04952 41
 
< 0.1%
0,04561 41
 
< 0.1%
0,04619 41
 
< 0.1%
0,08015 39
 
< 0.1%
0,04739 39
 
< 0.1%
Other values (61410) 375525
82.0%
(Missing) 82053
 
17.9%

Length

2023-04-23T20:24:52.269319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,05145 54
 
< 0.1%
0,04243 54
 
< 0.1%
0,03746 53
 
< 0.1%
0,04964 52
 
< 0.1%
0,04739 52
 
< 0.1%
0,03598 52
 
< 0.1%
0,00872 51
 
< 0.1%
0,04561 50
 
< 0.1%
0,03638 49
 
< 0.1%
0,04535 49
 
< 0.1%
Other values (36735) 375425
99.9%

Most occurring characters

ValueCountFrequency (%)
0 748505
27.1%
, 375941
13.6%
1 261938
 
9.5%
2 203716
 
7.4%
3 169302
 
6.1%
4 158080
 
5.7%
5 149218
 
5.4%
6 146196
 
5.3%
7 142187
 
5.2%
8 140312
 
5.1%
Other values (2) 262184
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2255646
81.8%
Other Punctuation 375941
 
13.6%
Dash Punctuation 125992
 
4.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 748505
33.2%
1 261938
 
11.6%
2 203716
 
9.0%
3 169302
 
7.5%
4 158080
 
7.0%
5 149218
 
6.6%
6 146196
 
6.5%
7 142187
 
6.3%
8 140312
 
6.2%
9 136192
 
6.0%
Other Punctuation
ValueCountFrequency (%)
, 375941
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 125992
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2757579
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 748505
27.1%
, 375941
13.6%
1 261938
 
9.5%
2 203716
 
7.4%
3 169302
 
6.1%
4 158080
 
5.7%
5 149218
 
5.4%
6 146196
 
5.3%
7 142187
 
5.2%
8 140312
 
5.1%
Other values (2) 262184
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2757579
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 748505
27.1%
, 375941
13.6%
1 261938
 
9.5%
2 203716
 
7.4%
3 169302
 
6.1%
4 158080
 
5.7%
5 149218
 
5.4%
6 146196
 
5.3%
7 142187
 
5.2%
8 140312
 
5.1%
Other values (2) 262184
 
9.5%

Gaze direction left Y
Categorical

HIGH CARDINALITY  MISSING 

Distinct55109
Distinct (%)14.7%
Missing82053
Missing (%)17.9%
Memory size3.5 MiB
0,06222
 
36
0,01155
 
35
-0,03982
 
34
0,03073
 
33
0,01229
 
33
Other values (55104)
375770 

Length

Max length8
Median length7
Mean length7.3271444
Min length7

Characters and Unicode

Total characters2754574
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8584 ?
Unique (%)2.3%

Sample

1st row0,07270
2nd row0,07029
3rd row0,07778
4th row0,08342
5th row0,07999

Common Values

ValueCountFrequency (%)
0,06222 36
 
< 0.1%
0,01155 35
 
< 0.1%
-0,03982 34
 
< 0.1%
0,03073 33
 
< 0.1%
0,01229 33
 
< 0.1%
0,07534 33
 
< 0.1%
0,01592 33
 
< 0.1%
0,06269 32
 
< 0.1%
-0,03717 32
 
< 0.1%
0,05161 32
 
< 0.1%
Other values (55099) 375608
82.0%
(Missing) 82053
 
17.9%

Length

2023-04-23T20:24:52.379123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,03717 52
 
< 0.1%
0,01595 50
 
< 0.1%
0,04519 50
 
< 0.1%
0,06051 50
 
< 0.1%
0,03766 49
 
< 0.1%
0,06026 49
 
< 0.1%
0,06269 48
 
< 0.1%
0,02235 47
 
< 0.1%
0,05985 47
 
< 0.1%
0,05161 47
 
< 0.1%
Other values (36004) 375452
99.9%

Most occurring characters

ValueCountFrequency (%)
0 743194
27.0%
, 375941
13.6%
1 270461
 
9.8%
2 198721
 
7.2%
3 166634
 
6.0%
4 155107
 
5.6%
5 150881
 
5.5%
6 148427
 
5.4%
7 144186
 
5.2%
8 140331
 
5.1%
Other values (2) 260691
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2255646
81.9%
Other Punctuation 375941
 
13.6%
Dash Punctuation 122987
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 743194
32.9%
1 270461
 
12.0%
2 198721
 
8.8%
3 166634
 
7.4%
4 155107
 
6.9%
5 150881
 
6.7%
6 148427
 
6.6%
7 144186
 
6.4%
8 140331
 
6.2%
9 137704
 
6.1%
Other Punctuation
ValueCountFrequency (%)
, 375941
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 122987
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2754574
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 743194
27.0%
, 375941
13.6%
1 270461
 
9.8%
2 198721
 
7.2%
3 166634
 
6.0%
4 155107
 
5.6%
5 150881
 
5.5%
6 148427
 
5.4%
7 144186
 
5.2%
8 140331
 
5.1%
Other values (2) 260691
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2754574
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 743194
27.0%
, 375941
13.6%
1 270461
 
9.8%
2 198721
 
7.2%
3 166634
 
6.0%
4 155107
 
5.6%
5 150881
 
5.5%
6 148427
 
5.4%
7 144186
 
5.2%
8 140331
 
5.1%
Other values (2) 260691
 
9.5%

Gaze direction left Z
Categorical

HIGH CARDINALITY  MISSING 

Distinct11729
Distinct (%)3.1%
Missing82053
Missing (%)17.9%
Memory size3.5 MiB
-0,99899
 
307
-0,99904
 
297
-0,99975
 
296
-0,99961
 
290
-0,99870
 
289
Other values (11724)
374462 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters3007528
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1700 ?
Unique (%)0.5%

Sample

1st row-0,99626
2nd row-0,99639
3rd row-0,99556
4th row-0,99530
5th row-0,99562

Common Values

ValueCountFrequency (%)
-0,99899 307
 
0.1%
-0,99904 297
 
0.1%
-0,99975 296
 
0.1%
-0,99961 290
 
0.1%
-0,99870 289
 
0.1%
-0,99796 288
 
0.1%
-0,99966 288
 
0.1%
-0,99965 287
 
0.1%
-0,99960 286
 
0.1%
-0,99964 285
 
0.1%
Other values (11719) 373028
81.4%
(Missing) 82053
 
17.9%

Length

2023-04-23T20:24:52.496863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,99899 307
 
0.1%
0,99904 297
 
0.1%
0,99975 296
 
0.1%
0,99961 290
 
0.1%
0,99870 289
 
0.1%
0,99796 288
 
0.1%
0,99966 288
 
0.1%
0,99965 287
 
0.1%
0,99960 286
 
0.1%
0,99964 285
 
0.1%
Other values (11719) 373028
99.2%

Most occurring characters

ValueCountFrequency (%)
9 665292
22.1%
0 481744
16.0%
- 375941
12.5%
, 375941
12.5%
8 210686
 
7.0%
7 164260
 
5.5%
6 143475
 
4.8%
5 133047
 
4.4%
4 124872
 
4.2%
3 116035
 
3.9%
Other values (2) 216235
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2255646
75.0%
Dash Punctuation 375941
 
12.5%
Other Punctuation 375941
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 665292
29.5%
0 481744
21.4%
8 210686
 
9.3%
7 164260
 
7.3%
6 143475
 
6.4%
5 133047
 
5.9%
4 124872
 
5.5%
3 116035
 
5.1%
2 108386
 
4.8%
1 107849
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 375941
100.0%
Other Punctuation
ValueCountFrequency (%)
, 375941
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3007528
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 665292
22.1%
0 481744
16.0%
- 375941
12.5%
, 375941
12.5%
8 210686
 
7.0%
7 164260
 
5.5%
6 143475
 
4.8%
5 133047
 
4.4%
4 124872
 
4.2%
3 116035
 
3.9%
Other values (2) 216235
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3007528
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 665292
22.1%
0 481744
16.0%
- 375941
12.5%
, 375941
12.5%
8 210686
 
7.0%
7 164260
 
5.5%
6 143475
 
4.8%
5 133047
 
4.4%
4 124872
 
4.2%
3 116035
 
3.9%
Other values (2) 216235
 
7.2%

Gaze direction right X
Categorical

HIGH CARDINALITY  MISSING 

Distinct60429
Distinct (%)17.7%
Missing117001
Missing (%)25.5%
Memory size3.5 MiB
0,06559
 
38
0,06378
 
38
0,03052
 
36
0,06361
 
35
0,05632
 
35
Other values (60424)
340811 

Length

Max length8
Median length7
Mean length7.4977815
Min length7

Characters and Unicode

Total characters2556691
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11923 ?
Unique (%)3.5%

Sample

1st row-0,02409
2nd row-0,02419
3rd row-0,02258
4th row-0,02248
5th row-0,02106

Common Values

ValueCountFrequency (%)
0,06559 38
 
< 0.1%
0,06378 38
 
< 0.1%
0,03052 36
 
< 0.1%
0,06361 35
 
< 0.1%
0,05632 35
 
< 0.1%
-0,04961 34
 
< 0.1%
0,04133 33
 
< 0.1%
0,05424 33
 
< 0.1%
0,04646 33
 
< 0.1%
0,03388 32
 
< 0.1%
Other values (60419) 340646
74.4%
(Missing) 117001
 
25.5%

Length

2023-04-23T20:24:52.616533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,02697 53
 
< 0.1%
0,03388 53
 
< 0.1%
0,04078 52
 
< 0.1%
0,06378 51
 
< 0.1%
0,04802 49
 
< 0.1%
0,05632 48
 
< 0.1%
0,03052 47
 
< 0.1%
0,04525 47
 
< 0.1%
0,04611 46
 
< 0.1%
0,04133 46
 
< 0.1%
Other values (35481) 340501
99.9%

Most occurring characters

ValueCountFrequency (%)
0 678170
26.5%
, 340993
13.3%
1 236722
 
9.3%
2 181982
 
7.1%
- 169740
 
6.6%
3 150817
 
5.9%
4 139796
 
5.5%
5 137525
 
5.4%
6 135923
 
5.3%
7 131168
 
5.1%
Other values (2) 253855
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2045958
80.0%
Other Punctuation 340993
 
13.3%
Dash Punctuation 169740
 
6.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 678170
33.1%
1 236722
 
11.6%
2 181982
 
8.9%
3 150817
 
7.4%
4 139796
 
6.8%
5 137525
 
6.7%
6 135923
 
6.6%
7 131168
 
6.4%
8 127866
 
6.2%
9 125989
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 340993
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 169740
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2556691
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 678170
26.5%
, 340993
13.3%
1 236722
 
9.3%
2 181982
 
7.1%
- 169740
 
6.6%
3 150817
 
5.9%
4 139796
 
5.5%
5 137525
 
5.4%
6 135923
 
5.3%
7 131168
 
5.1%
Other values (2) 253855
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2556691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 678170
26.5%
, 340993
13.3%
1 236722
 
9.3%
2 181982
 
7.1%
- 169740
 
6.6%
3 150817
 
5.9%
4 139796
 
5.5%
5 137525
 
5.4%
6 135923
 
5.3%
7 131168
 
5.1%
Other values (2) 253855
 
9.9%

Gaze direction right Y
Categorical

HIGH CARDINALITY  MISSING 

Distinct53969
Distinct (%)15.8%
Missing117001
Missing (%)25.5%
Memory size3.5 MiB
0,02833
 
39
0,00285
 
37
0,07343
 
35
0,01445
 
34
-0,02034
 
34
Other values (53964)
340814 

Length

Max length8
Median length7
Mean length7.3393853
Min length7

Characters and Unicode

Total characters2502679
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9343 ?
Unique (%)2.7%

Sample

1st row0,04899
2nd row0,05181
3rd row0,05768
4th row0,05779
5th row0,05694

Common Values

ValueCountFrequency (%)
0,02833 39
 
< 0.1%
0,00285 37
 
< 0.1%
0,07343 35
 
< 0.1%
0,01445 34
 
< 0.1%
-0,02034 34
 
< 0.1%
-0,00275 34
 
< 0.1%
0,09437 34
 
< 0.1%
0,01983 34
 
< 0.1%
0,06944 34
 
< 0.1%
0,01352 33
 
< 0.1%
Other values (53959) 340645
74.4%
(Missing) 117001
 
25.5%

Length

2023-04-23T20:24:52.730559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,01594 53
 
< 0.1%
0,01448 50
 
< 0.1%
0,01977 50
 
< 0.1%
0,02582 49
 
< 0.1%
0,06136 49
 
< 0.1%
0,00285 49
 
< 0.1%
0,01054 48
 
< 0.1%
0,01485 48
 
< 0.1%
0,03583 48
 
< 0.1%
0,00808 47
 
< 0.1%
Other values (34919) 340502
99.9%

Most occurring characters

ValueCountFrequency (%)
0 687765
27.5%
, 340993
13.6%
1 241063
 
9.6%
2 177621
 
7.1%
3 149598
 
6.0%
4 139739
 
5.6%
5 137200
 
5.5%
6 133749
 
5.3%
7 129080
 
5.2%
8 126331
 
5.0%
Other values (2) 239540
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2045958
81.8%
Other Punctuation 340993
 
13.6%
Dash Punctuation 115728
 
4.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 687765
33.6%
1 241063
 
11.8%
2 177621
 
8.7%
3 149598
 
7.3%
4 139739
 
6.8%
5 137200
 
6.7%
6 133749
 
6.5%
7 129080
 
6.3%
8 126331
 
6.2%
9 123812
 
6.1%
Other Punctuation
ValueCountFrequency (%)
, 340993
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 115728
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2502679
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 687765
27.5%
, 340993
13.6%
1 241063
 
9.6%
2 177621
 
7.1%
3 149598
 
6.0%
4 139739
 
5.6%
5 137200
 
5.5%
6 133749
 
5.3%
7 129080
 
5.2%
8 126331
 
5.0%
Other values (2) 239540
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2502679
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 687765
27.5%
, 340993
13.6%
1 241063
 
9.6%
2 177621
 
7.1%
3 149598
 
6.0%
4 139739
 
5.6%
5 137200
 
5.5%
6 133749
 
5.3%
7 129080
 
5.2%
8 126331
 
5.0%
Other values (2) 239540
 
9.6%

Gaze direction right Z
Categorical

HIGH CARDINALITY  MISSING 

Distinct11241
Distinct (%)3.3%
Missing117001
Missing (%)25.5%
Memory size3.5 MiB
-0,99909
 
314
-0,99943
 
289
-0,99950
 
288
-0,99878
 
287
-0,99929
 
286
Other values (11236)
339529 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters2727944
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1745 ?
Unique (%)0.5%

Sample

1st row-0,99851
2nd row-0,99836
3rd row-0,99808
4th row-0,99808
5th row-0,99816

Common Values

ValueCountFrequency (%)
-0,99909 314
 
0.1%
-0,99943 289
 
0.1%
-0,99950 288
 
0.1%
-0,99878 287
 
0.1%
-0,99929 286
 
0.1%
-0,99940 285
 
0.1%
-0,99945 278
 
0.1%
-0,99880 278
 
0.1%
-0,99931 276
 
0.1%
-0,99932 276
 
0.1%
Other values (11231) 338136
73.8%
(Missing) 117001
 
25.5%

Length

2023-04-23T20:24:52.841518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,99909 314
 
0.1%
0,99943 289
 
0.1%
0,99950 288
 
0.1%
0,99878 287
 
0.1%
0,99929 286
 
0.1%
0,99940 285
 
0.1%
0,99945 278
 
0.1%
0,99880 278
 
0.1%
0,99931 276
 
0.1%
0,99932 276
 
0.1%
Other values (11231) 338136
99.2%

Most occurring characters

ValueCountFrequency (%)
9 615759
22.6%
0 433288
15.9%
- 340993
12.5%
, 340993
12.5%
8 184080
 
6.7%
7 150823
 
5.5%
6 132453
 
4.9%
5 118916
 
4.4%
4 110416
 
4.0%
3 104620
 
3.8%
Other values (2) 195603
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2045958
75.0%
Dash Punctuation 340993
 
12.5%
Other Punctuation 340993
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 615759
30.1%
0 433288
21.2%
8 184080
 
9.0%
7 150823
 
7.4%
6 132453
 
6.5%
5 118916
 
5.8%
4 110416
 
5.4%
3 104620
 
5.1%
2 99943
 
4.9%
1 95660
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 340993
100.0%
Other Punctuation
ValueCountFrequency (%)
, 340993
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2727944
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 615759
22.6%
0 433288
15.9%
- 340993
12.5%
, 340993
12.5%
8 184080
 
6.7%
7 150823
 
5.5%
6 132453
 
4.9%
5 118916
 
4.4%
4 110416
 
4.0%
3 104620
 
3.8%
Other values (2) 195603
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2727944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 615759
22.6%
0 433288
15.9%
- 340993
12.5%
, 340993
12.5%
8 184080
 
6.7%
7 150823
 
5.5%
6 132453
 
4.9%
5 118916
 
4.4%
4 110416
 
4.0%
3 104620
 
3.8%
Other values (2) 195603
 
7.2%

Pupil diameter left
Categorical

HIGH CARDINALITY  MISSING 

Distinct471
Distinct (%)0.4%
Missing336313
Missing (%)73.4%
Memory size3.5 MiB
3,21
 
1201
3,16
 
1129
3,24
 
1128
3,20
 
1121
3,28
 
1113
Other values (466)
115989 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters486724
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)< 0.1%

Sample

1st row3,00
2nd row2,88
3rd row2,96
4th row2,98
5th row3,15

Common Values

ValueCountFrequency (%)
3,21 1201
 
0.3%
3,16 1129
 
0.2%
3,24 1128
 
0.2%
3,20 1121
 
0.2%
3,28 1113
 
0.2%
3,22 1107
 
0.2%
3,34 1107
 
0.2%
3,08 1095
 
0.2%
3,31 1094
 
0.2%
3,19 1089
 
0.2%
Other values (461) 110497
 
24.1%
(Missing) 336313
73.4%

Length

2023-04-23T20:24:52.942645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3,21 1201
 
1.0%
3,16 1129
 
0.9%
3,24 1128
 
0.9%
3,20 1121
 
0.9%
3,28 1113
 
0.9%
3,22 1107
 
0.9%
3,34 1107
 
0.9%
3,08 1095
 
0.9%
3,31 1094
 
0.9%
3,19 1089
 
0.9%
Other values (461) 110497
90.8%

Most occurring characters

ValueCountFrequency (%)
, 121681
25.0%
3 106943
22.0%
2 51336
10.5%
4 33996
 
7.0%
5 25759
 
5.3%
1 25449
 
5.2%
0 24612
 
5.1%
6 24488
 
5.0%
7 24409
 
5.0%
9 24406
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 365043
75.0%
Other Punctuation 121681
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 106943
29.3%
2 51336
14.1%
4 33996
 
9.3%
5 25759
 
7.1%
1 25449
 
7.0%
0 24612
 
6.7%
6 24488
 
6.7%
7 24409
 
6.7%
9 24406
 
6.7%
8 23645
 
6.5%
Other Punctuation
ValueCountFrequency (%)
, 121681
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 486724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 121681
25.0%
3 106943
22.0%
2 51336
10.5%
4 33996
 
7.0%
5 25759
 
5.3%
1 25449
 
5.2%
0 24612
 
5.1%
6 24488
 
5.0%
7 24409
 
5.0%
9 24406
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 486724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 121681
25.0%
3 106943
22.0%
2 51336
10.5%
4 33996
 
7.0%
5 25759
 
5.3%
1 25449
 
5.2%
0 24612
 
5.1%
6 24488
 
5.0%
7 24409
 
5.0%
9 24406
 
5.0%

Pupil diameter right
Categorical

HIGH CARDINALITY  MISSING 

Distinct509
Distinct (%)0.5%
Missing348819
Missing (%)76.2%
Memory size3.5 MiB
3,51
 
884
3,06
 
880
3,49
 
880
3,31
 
878
3,10
 
876
Other values (504)
104777 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters436700
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)< 0.1%

Sample

1st row3,16
2nd row3,16
3rd row3,11
4th row3,05
5th row2,99

Common Values

ValueCountFrequency (%)
3,51 884
 
0.2%
3,06 880
 
0.2%
3,49 880
 
0.2%
3,31 878
 
0.2%
3,10 876
 
0.2%
3,41 860
 
0.2%
3,57 841
 
0.2%
3,53 839
 
0.2%
3,37 832
 
0.2%
3,21 825
 
0.2%
Other values (499) 100580
 
22.0%
(Missing) 348819
76.2%

Length

2023-04-23T20:24:53.046588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3,51 884
 
0.8%
3,49 880
 
0.8%
3,06 880
 
0.8%
3,31 878
 
0.8%
3,10 876
 
0.8%
3,41 860
 
0.8%
3,57 841
 
0.8%
3,53 839
 
0.8%
3,37 832
 
0.8%
3,21 825
 
0.8%
Other values (499) 100580
92.1%

Most occurring characters

ValueCountFrequency (%)
, 109175
25.0%
3 91701
21.0%
2 43558
 
10.0%
4 34442
 
7.9%
1 23561
 
5.4%
9 23360
 
5.3%
0 22806
 
5.2%
8 22446
 
5.1%
5 22244
 
5.1%
7 21895
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 327525
75.0%
Other Punctuation 109175
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 91701
28.0%
2 43558
13.3%
4 34442
 
10.5%
1 23561
 
7.2%
9 23360
 
7.1%
0 22806
 
7.0%
8 22446
 
6.9%
5 22244
 
6.8%
7 21895
 
6.7%
6 21512
 
6.6%
Other Punctuation
ValueCountFrequency (%)
, 109175
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 436700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 109175
25.0%
3 91701
21.0%
2 43558
 
10.0%
4 34442
 
7.9%
1 23561
 
5.4%
9 23360
 
5.3%
0 22806
 
5.2%
8 22446
 
5.1%
5 22244
 
5.1%
7 21895
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 436700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 109175
25.0%
3 91701
21.0%
2 43558
 
10.0%
4 34442
 
7.9%
1 23561
 
5.4%
9 23360
 
5.3%
0 22806
 
5.2%
8 22446
 
5.1%
5 22244
 
5.1%
7 21895
 
5.0%

Validity left
Categorical

Distinct2
Distinct (%)< 0.1%
Missing6707
Missing (%)1.5%
Memory size3.5 MiB
Valid
375941 
Invalid
75346 

Length

Max length7
Median length5
Mean length5.3339161
Min length5

Characters and Unicode

Total characters2407127
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowValid
2nd rowValid
3rd rowValid
4th rowValid
5th rowValid

Common Values

ValueCountFrequency (%)
Valid 375941
82.1%
Invalid 75346
 
16.5%
(Missing) 6707
 
1.5%

Length

2023-04-23T20:24:53.154338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T20:24:53.271713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
valid 375941
83.3%
invalid 75346
 
16.7%

Most occurring characters

ValueCountFrequency (%)
a 451287
18.7%
l 451287
18.7%
i 451287
18.7%
d 451287
18.7%
V 375941
15.6%
I 75346
 
3.1%
n 75346
 
3.1%
v 75346
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1955840
81.3%
Uppercase Letter 451287
 
18.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 451287
23.1%
l 451287
23.1%
i 451287
23.1%
d 451287
23.1%
n 75346
 
3.9%
v 75346
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
V 375941
83.3%
I 75346
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 2407127
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 451287
18.7%
l 451287
18.7%
i 451287
18.7%
d 451287
18.7%
V 375941
15.6%
I 75346
 
3.1%
n 75346
 
3.1%
v 75346
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2407127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 451287
18.7%
l 451287
18.7%
i 451287
18.7%
d 451287
18.7%
V 375941
15.6%
I 75346
 
3.1%
n 75346
 
3.1%
v 75346
 
3.1%

Validity right
Categorical

Distinct2
Distinct (%)< 0.1%
Missing6707
Missing (%)1.5%
Memory size3.5 MiB
Valid
340993 
Invalid
110294 

Length

Max length7
Median length5
Mean length5.4887976
Min length5

Characters and Unicode

Total characters2477023
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowValid
2nd rowValid
3rd rowValid
4th rowValid
5th rowValid

Common Values

ValueCountFrequency (%)
Valid 340993
74.5%
Invalid 110294
 
24.1%
(Missing) 6707
 
1.5%

Length

2023-04-23T20:24:53.366636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T20:24:53.482008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
valid 340993
75.6%
invalid 110294
 
24.4%

Most occurring characters

ValueCountFrequency (%)
a 451287
18.2%
l 451287
18.2%
i 451287
18.2%
d 451287
18.2%
V 340993
13.8%
I 110294
 
4.5%
n 110294
 
4.5%
v 110294
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2025736
81.8%
Uppercase Letter 451287
 
18.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 451287
22.3%
l 451287
22.3%
i 451287
22.3%
d 451287
22.3%
n 110294
 
5.4%
v 110294
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
V 340993
75.6%
I 110294
 
24.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 2477023
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 451287
18.2%
l 451287
18.2%
i 451287
18.2%
d 451287
18.2%
V 340993
13.8%
I 110294
 
4.5%
n 110294
 
4.5%
v 110294
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2477023
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 451287
18.2%
l 451287
18.2%
i 451287
18.2%
d 451287
18.2%
V 340993
13.8%
I 110294
 
4.5%
n 110294
 
4.5%
v 110294
 
4.5%

Eye position left X (DACSmm)
Categorical

HIGH CARDINALITY  MISSING 

Distinct1742
Distinct (%)0.5%
Missing82053
Missing (%)17.9%
Memory size3.5 MiB
191,9
 
1312
191,7
 
1203
192,1
 
1111
191,8
 
1098
192,0
 
1075
Other values (1737)
370142 

Length

Max length5
Median length5
Mean length4.9992233
Min length4

Characters and Unicode

Total characters1879413
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)< 0.1%

Sample

1st row206,3
2nd row206,3
3rd row206,3
4th row206,2
5th row206,2

Common Values

ValueCountFrequency (%)
191,9 1312
 
0.3%
191,7 1203
 
0.3%
192,1 1111
 
0.2%
191,8 1098
 
0.2%
192,0 1075
 
0.2%
191,6 1074
 
0.2%
228,5 1034
 
0.2%
228,6 951
 
0.2%
231,9 936
 
0.2%
232,1 930
 
0.2%
Other values (1732) 365217
79.7%
(Missing) 82053
 
17.9%

Length

2023-04-23T20:24:53.572258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
191,9 1312
 
0.3%
191,7 1203
 
0.3%
192,1 1111
 
0.3%
191,8 1098
 
0.3%
192,0 1075
 
0.3%
191,6 1074
 
0.3%
228,5 1034
 
0.3%
228,6 951
 
0.3%
231,9 936
 
0.2%
232,1 930
 
0.2%
Other values (1732) 365217
97.1%

Most occurring characters

ValueCountFrequency (%)
, 375941
20.0%
2 351367
18.7%
1 268669
14.3%
9 139050
 
7.4%
0 122221
 
6.5%
8 109128
 
5.8%
3 108358
 
5.8%
7 108325
 
5.8%
6 107192
 
5.7%
4 103832
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1503472
80.0%
Other Punctuation 375941
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 351367
23.4%
1 268669
17.9%
9 139050
 
9.2%
0 122221
 
8.1%
8 109128
 
7.3%
3 108358
 
7.2%
7 108325
 
7.2%
6 107192
 
7.1%
4 103832
 
6.9%
5 85330
 
5.7%
Other Punctuation
ValueCountFrequency (%)
, 375941
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1879413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 375941
20.0%
2 351367
18.7%
1 268669
14.3%
9 139050
 
7.4%
0 122221
 
6.5%
8 109128
 
5.8%
3 108358
 
5.8%
7 108325
 
5.8%
6 107192
 
5.7%
4 103832
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1879413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 375941
20.0%
2 351367
18.7%
1 268669
14.3%
9 139050
 
7.4%
0 122221
 
6.5%
8 109128
 
5.8%
3 108358
 
5.8%
7 108325
 
5.8%
6 107192
 
5.7%
4 103832
 
5.5%

Eye position left Y (DACSmm)
Categorical

HIGH CARDINALITY  MISSING 

Distinct2573
Distinct (%)0.7%
Missing82053
Missing (%)17.9%
Memory size3.5 MiB
29,6
 
826
137,0
 
817
119,1
 
797
29,7
 
791
119,2
 
787
Other values (2568)
371923 

Length

Max length6
Median length4
Mean length4.4150093
Min length3

Characters and Unicode

Total characters1659783
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)< 0.1%

Sample

1st row53,7
2nd row53,6
3rd row53,6
4th row53,6
5th row53,5

Common Values

ValueCountFrequency (%)
29,6 826
 
0.2%
137,0 817
 
0.2%
119,1 797
 
0.2%
29,7 791
 
0.2%
119,2 787
 
0.2%
29,9 779
 
0.2%
30,8 769
 
0.2%
30,4 764
 
0.2%
31,1 760
 
0.2%
136,9 758
 
0.2%
Other values (2563) 368093
80.4%
(Missing) 82053
 
17.9%

Length

2023-04-23T20:24:53.697834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
29,6 909
 
0.2%
29,7 882
 
0.2%
29,9 871
 
0.2%
29,5 854
 
0.2%
29,8 849
 
0.2%
30,8 843
 
0.2%
30,4 839
 
0.2%
31,0 831
 
0.2%
31,1 831
 
0.2%
31,2 820
 
0.2%
Other values (1663) 367412
97.7%

Most occurring characters

ValueCountFrequency (%)
, 375941
22.7%
1 277889
16.7%
3 156426
9.4%
2 142366
 
8.6%
7 110876
 
6.7%
4 107905
 
6.5%
8 98326
 
5.9%
0 94661
 
5.7%
6 92457
 
5.6%
9 90876
 
5.5%
Other values (2) 112060
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1261897
76.0%
Other Punctuation 375941
 
22.7%
Dash Punctuation 21945
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 277889
22.0%
3 156426
12.4%
2 142366
11.3%
7 110876
 
8.8%
4 107905
 
8.6%
8 98326
 
7.8%
0 94661
 
7.5%
6 92457
 
7.3%
9 90876
 
7.2%
5 90115
 
7.1%
Other Punctuation
ValueCountFrequency (%)
, 375941
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21945
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1659783
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 375941
22.7%
1 277889
16.7%
3 156426
9.4%
2 142366
 
8.6%
7 110876
 
6.7%
4 107905
 
6.5%
8 98326
 
5.9%
0 94661
 
5.7%
6 92457
 
5.6%
9 90876
 
5.5%
Other values (2) 112060
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1659783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 375941
22.7%
1 277889
16.7%
3 156426
9.4%
2 142366
 
8.6%
7 110876
 
6.7%
4 107905
 
6.5%
8 98326
 
5.9%
0 94661
 
5.7%
6 92457
 
5.6%
9 90876
 
5.5%
Other values (2) 112060
 
6.8%

Eye position left Z (DACSmm)
Categorical

HIGH CARDINALITY  MISSING 

Distinct4834
Distinct (%)1.3%
Missing82053
Missing (%)17.9%
Memory size3.5 MiB
750,1
 
732
749,3
 
719
749,7
 
706
750,3
 
691
749,6
 
681
Other values (4829)
372412 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters1879705
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)< 0.1%

Sample

1st row585,0
2nd row585,1
3rd row585,1
4th row585,3
5th row585,5

Common Values

ValueCountFrequency (%)
750,1 732
 
0.2%
749,3 719
 
0.2%
749,7 706
 
0.2%
750,3 691
 
0.2%
749,6 681
 
0.1%
750,0 664
 
0.1%
749,9 661
 
0.1%
749,2 645
 
0.1%
749,5 631
 
0.1%
750,4 630
 
0.1%
Other values (4824) 369181
80.6%
(Missing) 82053
 
17.9%

Length

2023-04-23T20:24:53.814366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
750,1 732
 
0.2%
749,3 719
 
0.2%
749,7 706
 
0.2%
750,3 691
 
0.2%
749,6 681
 
0.2%
750,0 664
 
0.2%
749,9 661
 
0.2%
749,2 645
 
0.2%
749,5 631
 
0.2%
750,4 630
 
0.2%
Other values (4824) 369181
98.2%

Most occurring characters

ValueCountFrequency (%)
, 375941
20.0%
7 251452
13.4%
4 205110
10.9%
6 184095
9.8%
5 169599
9.0%
8 155464
8.3%
3 117605
 
6.3%
0 111266
 
5.9%
1 109133
 
5.8%
9 104278
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1503764
80.0%
Other Punctuation 375941
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 251452
16.7%
4 205110
13.6%
6 184095
12.2%
5 169599
11.3%
8 155464
10.3%
3 117605
7.8%
0 111266
7.4%
1 109133
7.3%
9 104278
6.9%
2 95762
 
6.4%
Other Punctuation
ValueCountFrequency (%)
, 375941
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1879705
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 375941
20.0%
7 251452
13.4%
4 205110
10.9%
6 184095
9.8%
5 169599
9.0%
8 155464
8.3%
3 117605
 
6.3%
0 111266
 
5.9%
1 109133
 
5.8%
9 104278
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1879705
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 375941
20.0%
7 251452
13.4%
4 205110
10.9%
6 184095
9.8%
5 169599
9.0%
8 155464
8.3%
3 117605
 
6.3%
0 111266
 
5.9%
1 109133
 
5.8%
9 104278
 
5.5%

Eye position right X (DACSmm)
Categorical

HIGH CARDINALITY  MISSING 

Distinct1808
Distinct (%)0.5%
Missing117001
Missing (%)25.5%
Memory size3.5 MiB
290,5
 
1419
294,3
 
1133
290,4
 
1114
290,8
 
1098
293,8
 
1084
Other values (1803)
335145 

Length

Max length5
Median length5
Mean length4.9994809
Min length4

Characters and Unicode

Total characters1704788
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st row272,8
2nd row272,8
3rd row272,9
4th row272,8
5th row272,8

Common Values

ValueCountFrequency (%)
290,5 1419
 
0.3%
294,3 1133
 
0.2%
290,4 1114
 
0.2%
290,8 1098
 
0.2%
293,8 1084
 
0.2%
290,7 1060
 
0.2%
293,9 1055
 
0.2%
294,2 1025
 
0.2%
293,7 1016
 
0.2%
258,8 1002
 
0.2%
Other values (1798) 329987
72.1%
(Missing) 117001
 
25.5%

Length

2023-04-23T20:24:53.928447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
290,5 1419
 
0.4%
294,3 1133
 
0.3%
290,4 1114
 
0.3%
290,8 1098
 
0.3%
293,8 1084
 
0.3%
290,7 1060
 
0.3%
293,9 1055
 
0.3%
294,2 1025
 
0.3%
293,7 1016
 
0.3%
258,8 1002
 
0.3%
Other values (1798) 329987
96.8%

Most occurring characters

ValueCountFrequency (%)
2 371523
21.8%
, 340993
20.0%
3 154856
9.1%
9 121231
 
7.1%
5 112800
 
6.6%
8 109515
 
6.4%
6 104974
 
6.2%
7 104423
 
6.1%
4 102112
 
6.0%
0 98614
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1363795
80.0%
Other Punctuation 340993
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 371523
27.2%
3 154856
11.4%
9 121231
 
8.9%
5 112800
 
8.3%
8 109515
 
8.0%
6 104974
 
7.7%
7 104423
 
7.7%
4 102112
 
7.5%
0 98614
 
7.2%
1 83747
 
6.1%
Other Punctuation
ValueCountFrequency (%)
, 340993
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1704788
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 371523
21.8%
, 340993
20.0%
3 154856
9.1%
9 121231
 
7.1%
5 112800
 
6.6%
8 109515
 
6.4%
6 104974
 
6.2%
7 104423
 
6.1%
4 102112
 
6.0%
0 98614
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1704788
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 371523
21.8%
, 340993
20.0%
3 154856
9.1%
9 121231
 
7.1%
5 112800
 
6.6%
8 109515
 
6.4%
6 104974
 
6.2%
7 104423
 
6.1%
4 102112
 
6.0%
0 98614
 
5.8%

Eye position right Y (DACSmm)
Categorical

HIGH CARDINALITY  MISSING 

Distinct2662
Distinct (%)0.8%
Missing117001
Missing (%)25.5%
Memory size3.5 MiB
26,9
 
1190
26,8
 
1107
26,6
 
1033
26,5
 
980
26,7
 
976
Other values (2657)
335707 

Length

Max length6
Median length4
Mean length4.3875212
Min length3

Characters and Unicode

Total characters1496114
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique197 ?
Unique (%)0.1%

Sample

1st row46,7
2nd row46,7
3rd row46,8
4th row46,9
5th row46,9

Common Values

ValueCountFrequency (%)
26,9 1190
 
0.3%
26,8 1107
 
0.2%
26,6 1033
 
0.2%
26,5 980
 
0.2%
26,7 976
 
0.2%
26,4 959
 
0.2%
27,0 931
 
0.2%
27,1 873
 
0.2%
27,2 871
 
0.2%
26,3 871
 
0.2%
Other values (2652) 331202
72.3%
(Missing) 117001
 
25.5%

Length

2023-04-23T20:24:54.036129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
26,9 1238
 
0.4%
26,8 1163
 
0.3%
26,6 1098
 
0.3%
26,5 1039
 
0.3%
26,7 1033
 
0.3%
26,4 1014
 
0.3%
27,0 983
 
0.3%
27,1 945
 
0.3%
27,2 933
 
0.3%
26,3 923
 
0.3%
Other values (1755) 330624
97.0%

Most occurring characters

ValueCountFrequency (%)
, 340993
22.8%
1 235547
15.7%
2 154876
10.4%
3 134404
 
9.0%
8 96866
 
6.5%
7 96622
 
6.5%
6 91920
 
6.1%
4 85375
 
5.7%
0 79664
 
5.3%
5 79496
 
5.3%
Other values (2) 100351
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1134005
75.8%
Other Punctuation 340993
 
22.8%
Dash Punctuation 21116
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 235547
20.8%
2 154876
13.7%
3 134404
11.9%
8 96866
8.5%
7 96622
8.5%
6 91920
 
8.1%
4 85375
 
7.5%
0 79664
 
7.0%
5 79496
 
7.0%
9 79235
 
7.0%
Other Punctuation
ValueCountFrequency (%)
, 340993
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21116
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1496114
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 340993
22.8%
1 235547
15.7%
2 154876
10.4%
3 134404
 
9.0%
8 96866
 
6.5%
7 96622
 
6.5%
6 91920
 
6.1%
4 85375
 
5.7%
0 79664
 
5.3%
5 79496
 
5.3%
Other values (2) 100351
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1496114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 340993
22.8%
1 235547
15.7%
2 154876
10.4%
3 134404
 
9.0%
8 96866
 
6.5%
7 96622
 
6.5%
6 91920
 
6.1%
4 85375
 
5.7%
0 79664
 
5.3%
5 79496
 
5.3%
Other values (2) 100351
 
6.7%

Eye position right Z (DACSmm)
Categorical

HIGH CARDINALITY  MISSING 

Distinct4955
Distinct (%)1.5%
Missing117001
Missing (%)25.5%
Memory size3.5 MiB
753,5
 
594
753,6
 
572
752,6
 
566
761,1
 
558
753,2
 
550
Other values (4950)
338153 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters1704965
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)< 0.1%

Sample

1st row595,5
2nd row595,4
3rd row595,3
4th row595,2
5th row595,1

Common Values

ValueCountFrequency (%)
753,5 594
 
0.1%
753,6 572
 
0.1%
752,6 566
 
0.1%
761,1 558
 
0.1%
753,2 550
 
0.1%
762,4 546
 
0.1%
433,9 528
 
0.1%
753,0 522
 
0.1%
760,0 519
 
0.1%
752,7 515
 
0.1%
Other values (4945) 335523
73.3%
(Missing) 117001
 
25.5%

Length

2023-04-23T20:24:54.149448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
753,5 594
 
0.2%
753,6 572
 
0.2%
752,6 566
 
0.2%
761,1 558
 
0.2%
753,2 550
 
0.2%
762,4 546
 
0.2%
433,9 528
 
0.2%
753,0 522
 
0.2%
760,0 519
 
0.2%
752,7 515
 
0.2%
Other values (4945) 335523
98.4%

Most occurring characters

ValueCountFrequency (%)
, 340993
20.0%
7 227916
13.4%
6 186929
11.0%
4 176554
10.4%
5 156347
9.2%
8 131278
 
7.7%
3 106630
 
6.3%
0 101977
 
6.0%
1 98234
 
5.8%
9 89177
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1363972
80.0%
Other Punctuation 340993
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 227916
16.7%
6 186929
13.7%
4 176554
12.9%
5 156347
11.5%
8 131278
9.6%
3 106630
7.8%
0 101977
7.5%
1 98234
7.2%
9 89177
 
6.5%
2 88930
 
6.5%
Other Punctuation
ValueCountFrequency (%)
, 340993
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1704965
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 340993
20.0%
7 227916
13.4%
6 186929
11.0%
4 176554
10.4%
5 156347
9.2%
8 131278
 
7.7%
3 106630
 
6.3%
0 101977
 
6.0%
1 98234
 
5.8%
9 89177
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1704965
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 340993
20.0%
7 227916
13.4%
6 186929
11.0%
4 176554
10.4%
5 156347
9.2%
8 131278
 
7.7%
3 106630
 
6.3%
0 101977
 
6.0%
1 98234
 
5.8%
9 89177
 
5.2%

Gaze point left X (DACSmm)
Categorical

HIGH CARDINALITY  MISSING 

Distinct5688
Distinct (%)1.5%
Missing82053
Missing (%)17.9%
Memory size3.5 MiB
234,6
 
388
235,1
 
367
236,3
 
359
233,9
 
357
234,9
 
353
Other values (5683)
374117 

Length

Max length5
Median length5
Mean length4.9183542
Min length3

Characters and Unicode

Total characters1849011
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique126 ?
Unique (%)< 0.1%

Sample

1st row233,7
2nd row234,3
3rd row237,4
4th row235,2
5th row234,7

Common Values

ValueCountFrequency (%)
234,6 388
 
0.1%
235,1 367
 
0.1%
236,3 359
 
0.1%
233,9 357
 
0.1%
234,9 353
 
0.1%
234,7 347
 
0.1%
234,2 343
 
0.1%
234,4 337
 
0.1%
233,1 335
 
0.1%
235,2 334
 
0.1%
Other values (5678) 372421
81.3%
(Missing) 82053
 
17.9%

Length

2023-04-23T20:24:54.265712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
234,6 388
 
0.1%
235,1 367
 
0.1%
236,3 359
 
0.1%
233,9 357
 
0.1%
234,9 353
 
0.1%
234,7 347
 
0.1%
234,2 343
 
0.1%
234,4 337
 
0.1%
233,1 335
 
0.1%
235,2 334
 
0.1%
Other values (5485) 372421
99.1%

Most occurring characters

ValueCountFrequency (%)
, 375941
20.3%
2 302215
16.3%
1 187357
10.1%
3 182727
9.9%
4 133999
 
7.2%
5 117879
 
6.4%
6 113197
 
6.1%
0 110635
 
6.0%
9 109122
 
5.9%
7 108992
 
5.9%
Other values (2) 106947
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1471594
79.6%
Other Punctuation 375941
 
20.3%
Dash Punctuation 1476
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 302215
20.5%
1 187357
12.7%
3 182727
12.4%
4 133999
9.1%
5 117879
 
8.0%
6 113197
 
7.7%
0 110635
 
7.5%
9 109122
 
7.4%
7 108992
 
7.4%
8 105471
 
7.2%
Other Punctuation
ValueCountFrequency (%)
, 375941
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1476
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1849011
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 375941
20.3%
2 302215
16.3%
1 187357
10.1%
3 182727
9.9%
4 133999
 
7.2%
5 117879
 
6.4%
6 113197
 
6.1%
0 110635
 
6.0%
9 109122
 
5.9%
7 108992
 
5.9%
Other values (2) 106947
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1849011
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 375941
20.3%
2 302215
16.3%
1 187357
10.1%
3 182727
9.9%
4 133999
 
7.2%
5 117879
 
6.4%
6 113197
 
6.1%
0 110635
 
6.0%
9 109122
 
5.9%
7 108992
 
5.9%
Other values (2) 106947
 
5.8%

Gaze point left Y (DACSmm)
Categorical

HIGH CARDINALITY  MISSING 

Distinct3918
Distinct (%)1.0%
Missing82053
Missing (%)17.9%
Memory size3.5 MiB
88,2
 
319
94,7
 
316
89,9
 
314
86,9
 
312
89,4
 
311
Other values (3913)
374369 

Length

Max length5
Median length5
Mean length4.5451121
Min length3

Characters and Unicode

Total characters1708694
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)< 0.1%

Sample

1st row96,3
2nd row94,9
3rd row99,3
4th row102,6
5th row100,5

Common Values

ValueCountFrequency (%)
88,2 319
 
0.1%
94,7 316
 
0.1%
89,9 314
 
0.1%
86,9 312
 
0.1%
89,4 311
 
0.1%
86,8 310
 
0.1%
86,6 308
 
0.1%
87,3 304
 
0.1%
86,2 301
 
0.1%
90,5 301
 
0.1%
Other values (3908) 372845
81.4%
(Missing) 82053
 
17.9%

Length

2023-04-23T20:24:54.393888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
88,2 319
 
0.1%
94,7 316
 
0.1%
89,9 314
 
0.1%
86,9 312
 
0.1%
89,4 311
 
0.1%
86,8 310
 
0.1%
86,6 308
 
0.1%
87,3 304
 
0.1%
86,2 301
 
0.1%
90,5 301
 
0.1%
Other values (3340) 372845
99.2%

Most occurring characters

ValueCountFrequency (%)
, 375941
22.0%
1 264756
15.5%
2 158355
9.3%
3 116600
 
6.8%
5 113381
 
6.6%
4 113263
 
6.6%
6 112884
 
6.6%
7 111892
 
6.5%
8 110173
 
6.4%
9 109080
 
6.4%
Other values (2) 122369
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1316982
77.1%
Other Punctuation 375941
 
22.0%
Dash Punctuation 15771
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 264756
20.1%
2 158355
12.0%
3 116600
8.9%
5 113381
8.6%
4 113263
8.6%
6 112884
8.6%
7 111892
8.5%
8 110173
8.4%
9 109080
8.3%
0 106598
8.1%
Other Punctuation
ValueCountFrequency (%)
, 375941
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15771
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1708694
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 375941
22.0%
1 264756
15.5%
2 158355
9.3%
3 116600
 
6.8%
5 113381
 
6.6%
4 113263
 
6.6%
6 112884
 
6.6%
7 111892
 
6.5%
8 110173
 
6.4%
9 109080
 
6.4%
Other values (2) 122369
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1708694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 375941
22.0%
1 264756
15.5%
2 158355
9.3%
3 116600
 
6.8%
5 113381
 
6.6%
4 113263
 
6.6%
6 112884
 
6.6%
7 111892
 
6.5%
8 110173
 
6.4%
9 109080
 
6.4%
Other values (2) 122369
 
7.2%

Gaze point right X (DACSmm)
Categorical

HIGH CARDINALITY  MISSING 

Distinct5773
Distinct (%)1.7%
Missing117001
Missing (%)25.5%
Memory size3.5 MiB
254,8
 
271
256,5
 
254
251,8
 
253
258,4
 
247
256,6
 
247
Other values (5768)
339721 

Length

Max length5
Median length5
Mean length4.9559346
Min length3

Characters and Unicode

Total characters1689939
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)< 0.1%

Sample

1st row258,5
2nd row258,4
3rd row259,4
4th row259,4
5th row260,2

Common Values

ValueCountFrequency (%)
254,8 271
 
0.1%
256,5 254
 
0.1%
251,8 253
 
0.1%
258,4 247
 
0.1%
256,6 247
 
0.1%
254,1 246
 
0.1%
252,5 246
 
0.1%
252,8 244
 
0.1%
255,0 243
 
0.1%
252,1 243
 
0.1%
Other values (5763) 338499
73.9%
(Missing) 117001
 
25.5%

Length

2023-04-23T20:24:54.515533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
254,8 271
 
0.1%
256,5 254
 
0.1%
251,8 253
 
0.1%
258,4 247
 
0.1%
256,6 247
 
0.1%
254,1 246
 
0.1%
252,5 246
 
0.1%
252,8 244
 
0.1%
255,0 243
 
0.1%
252,1 243
 
0.1%
Other values (5763) 338499
99.3%

Most occurring characters

ValueCountFrequency (%)
, 340993
20.2%
2 251739
14.9%
3 197638
11.7%
1 149927
8.9%
4 129060
 
7.6%
5 109500
 
6.5%
6 104566
 
6.2%
9 103160
 
6.1%
7 102198
 
6.0%
8 101425
 
6.0%
Other values (2) 99733
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1348934
79.8%
Other Punctuation 340993
 
20.2%
Dash Punctuation 12
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 251739
18.7%
3 197638
14.7%
1 149927
11.1%
4 129060
9.6%
5 109500
8.1%
6 104566
7.8%
9 103160
7.6%
7 102198
7.6%
8 101425
7.5%
0 99721
 
7.4%
Other Punctuation
ValueCountFrequency (%)
, 340993
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1689939
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 340993
20.2%
2 251739
14.9%
3 197638
11.7%
1 149927
8.9%
4 129060
 
7.6%
5 109500
 
6.5%
6 104566
 
6.2%
9 103160
 
6.1%
7 102198
 
6.0%
8 101425
 
6.0%
Other values (2) 99733
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1689939
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 340993
20.2%
2 251739
14.9%
3 197638
11.7%
1 149927
8.9%
4 129060
 
7.6%
5 109500
 
6.5%
6 104566
 
6.2%
9 103160
 
6.1%
7 102198
 
6.0%
8 101425
 
6.0%
Other values (2) 99733
 
5.9%

Gaze point right Y (DACSmm)
Categorical

HIGH CARDINALITY  MISSING 

Distinct3978
Distinct (%)1.2%
Missing117001
Missing (%)25.5%
Memory size3.5 MiB
81,3
 
309
79,3
 
294
79,0
 
288
78,6
 
283
88,0
 
281
Other values (3973)
339538 

Length

Max length5
Median length5
Mean length4.5179139
Min length3

Characters and Unicode

Total characters1540577
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)< 0.1%

Sample

1st row75,9
2nd row77,6
3rd row81,2
4th row81,3
5th row80,9

Common Values

ValueCountFrequency (%)
81,3 309
 
0.1%
79,3 294
 
0.1%
79,0 288
 
0.1%
78,6 283
 
0.1%
88,0 281
 
0.1%
87,9 280
 
0.1%
78,4 279
 
0.1%
82,6 277
 
0.1%
79,4 274
 
0.1%
84,4 272
 
0.1%
Other values (3968) 338156
73.8%
(Missing) 117001
 
25.5%

Length

2023-04-23T20:24:54.640610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
81,3 309
 
0.1%
79,3 294
 
0.1%
79,0 288
 
0.1%
78,6 283
 
0.1%
88,0 281
 
0.1%
87,9 280
 
0.1%
78,4 279
 
0.1%
82,6 277
 
0.1%
79,4 274
 
0.1%
84,4 272
 
0.1%
Other values (3390) 338156
99.2%

Most occurring characters

ValueCountFrequency (%)
, 340993
22.1%
1 244410
15.9%
2 139706
9.1%
3 101053
 
6.6%
8 100983
 
6.6%
7 100594
 
6.5%
5 100490
 
6.5%
4 99641
 
6.5%
6 98770
 
6.4%
0 97805
 
6.3%
Other values (2) 116132
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1180639
76.6%
Other Punctuation 340993
 
22.1%
Dash Punctuation 18945
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 244410
20.7%
2 139706
11.8%
3 101053
8.6%
8 100983
8.6%
7 100594
8.5%
5 100490
8.5%
4 99641
8.4%
6 98770
8.4%
0 97805
8.3%
9 97187
 
8.2%
Other Punctuation
ValueCountFrequency (%)
, 340993
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18945
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1540577
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 340993
22.1%
1 244410
15.9%
2 139706
9.1%
3 101053
 
6.6%
8 100983
 
6.6%
7 100594
 
6.5%
5 100490
 
6.5%
4 99641
 
6.5%
6 98770
 
6.4%
0 97805
 
6.3%
Other values (2) 116132
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1540577
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 340993
22.1%
1 244410
15.9%
2 139706
9.1%
3 101053
 
6.6%
8 100983
 
6.6%
7 100594
 
6.5%
5 100490
 
6.5%
4 99641
 
6.5%
6 98770
 
6.4%
0 97805
 
6.3%
Other values (2) 116132
 
7.5%

Gaze point X (MCSnorm)
Categorical

HIGH CARDINALITY  MISSING 

Distinct9841
Distinct (%)2.6%
Missing82366
Missing (%)18.0%
Memory size3.5 MiB
0,5172
 
198
0,5270
 
181
0,4797
 
177
0,5083
 
171
0,5057
 
170
Other values (9836)
374731 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters2253768
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique127 ?
Unique (%)< 0.1%

Sample

1st row0,4626
2nd row0,4633
3rd row0,4677
4th row0,4652
5th row0,4656

Common Values

ValueCountFrequency (%)
0,5172 198
 
< 0.1%
0,5270 181
 
< 0.1%
0,4797 177
 
< 0.1%
0,5083 171
 
< 0.1%
0,5057 170
 
< 0.1%
0,4815 170
 
< 0.1%
0,4796 166
 
< 0.1%
0,5436 165
 
< 0.1%
0,5077 165
 
< 0.1%
0,5301 165
 
< 0.1%
Other values (9831) 373900
81.6%
(Missing) 82366
 
18.0%

Length

2023-04-23T20:24:54.757087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,5172 198
 
0.1%
0,5270 181
 
< 0.1%
0,4797 177
 
< 0.1%
0,5083 171
 
< 0.1%
0,5057 170
 
< 0.1%
0,4815 170
 
< 0.1%
0,4796 166
 
< 0.1%
0,5077 165
 
< 0.1%
0,5301 165
 
< 0.1%
0,5436 165
 
< 0.1%
Other values (9831) 373900
99.5%

Most occurring characters

ValueCountFrequency (%)
0 495808
22.0%
, 375628
16.7%
4 214007
9.5%
5 211620
9.4%
3 158459
 
7.0%
6 152302
 
6.8%
2 138726
 
6.2%
1 132767
 
5.9%
7 131322
 
5.8%
8 127054
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1878140
83.3%
Other Punctuation 375628
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 495808
26.4%
4 214007
11.4%
5 211620
11.3%
3 158459
 
8.4%
6 152302
 
8.1%
2 138726
 
7.4%
1 132767
 
7.1%
7 131322
 
7.0%
8 127054
 
6.8%
9 116075
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 375628
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2253768
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 495808
22.0%
, 375628
16.7%
4 214007
9.5%
5 211620
9.4%
3 158459
 
7.0%
6 152302
 
6.8%
2 138726
 
6.2%
1 132767
 
5.9%
7 131322
 
5.8%
8 127054
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2253768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 495808
22.0%
, 375628
16.7%
4 214007
9.5%
5 211620
9.4%
3 158459
 
7.0%
6 152302
 
6.8%
2 138726
 
6.2%
1 132767
 
5.9%
7 131322
 
5.8%
8 127054
 
5.6%

Gaze point Y (MCSnorm)
Categorical

HIGH CARDINALITY  MISSING 

Distinct9924
Distinct (%)2.6%
Missing82366
Missing (%)18.0%
Memory size3.5 MiB
0,4297
 
137
0,3559
 
126
0,3378
 
115
0,3358
 
114
0,3442
 
113
Other values (9919)
375023 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters2253768
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique128 ?
Unique (%)< 0.1%

Sample

1st row0,2914
2nd row0,2918
3rd row0,3054
4th row0,3112
5th row0,3069

Common Values

ValueCountFrequency (%)
0,4297 137
 
< 0.1%
0,3559 126
 
< 0.1%
0,3378 115
 
< 0.1%
0,3358 114
 
< 0.1%
0,3442 113
 
< 0.1%
0,4060 112
 
< 0.1%
0,3541 110
 
< 0.1%
0,4304 110
 
< 0.1%
0,4006 109
 
< 0.1%
0,4102 109
 
< 0.1%
Other values (9914) 374473
81.8%
(Missing) 82366
 
18.0%

Length

2023-04-23T20:24:55.045282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,4297 137
 
< 0.1%
0,3559 126
 
< 0.1%
0,3378 115
 
< 0.1%
0,3358 114
 
< 0.1%
0,3442 113
 
< 0.1%
0,4060 112
 
< 0.1%
0,3541 110
 
< 0.1%
0,4304 110
 
< 0.1%
0,4363 109
 
< 0.1%
0,4006 109
 
< 0.1%
Other values (9914) 374473
99.7%

Most occurring characters

ValueCountFrequency (%)
0 512982
22.8%
, 375628
16.7%
3 184947
 
8.2%
4 180890
 
8.0%
2 177196
 
7.9%
5 161268
 
7.2%
1 153082
 
6.8%
6 138297
 
6.1%
7 130063
 
5.8%
8 124567
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1878140
83.3%
Other Punctuation 375628
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 512982
27.3%
3 184947
 
9.8%
4 180890
 
9.6%
2 177196
 
9.4%
5 161268
 
8.6%
1 153082
 
8.2%
6 138297
 
7.4%
7 130063
 
6.9%
8 124567
 
6.6%
9 114848
 
6.1%
Other Punctuation
ValueCountFrequency (%)
, 375628
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2253768
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 512982
22.8%
, 375628
16.7%
3 184947
 
8.2%
4 180890
 
8.0%
2 177196
 
7.9%
5 161268
 
7.2%
1 153082
 
6.8%
6 138297
 
6.1%
7 130063
 
5.8%
8 124567
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2253768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 512982
22.8%
, 375628
16.7%
3 184947
 
8.2%
4 180890
 
8.0%
2 177196
 
7.9%
5 161268
 
7.2%
1 153082
 
6.8%
6 138297
 
6.1%
7 130063
 
5.8%
8 124567
 
5.5%

Gaze point left X (MCSnorm)
Categorical

HIGH CARDINALITY  MISSING 

Distinct9875
Distinct (%)2.8%
Missing102522
Missing (%)22.4%
Memory size3.5 MiB
0,4309
 
169
0,4341
 
166
0,4444
 
164
0,4402
 
164
0,4436
 
162
Other values (9870)
354647 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters2132832
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique191 ?
Unique (%)0.1%

Sample

1st row0,4354
2nd row0,4368
3rd row0,4436
4th row0,4387
5th row0,4376

Common Values

ValueCountFrequency (%)
0,4309 169
 
< 0.1%
0,4341 166
 
< 0.1%
0,4444 164
 
< 0.1%
0,4402 164
 
< 0.1%
0,4436 162
 
< 0.1%
0,4458 158
 
< 0.1%
0,4583 157
 
< 0.1%
0,4585 157
 
< 0.1%
0,4524 156
 
< 0.1%
0,4355 155
 
< 0.1%
Other values (9865) 353864
77.3%
(Missing) 102522
 
22.4%

Length

2023-04-23T20:24:55.155166image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,4309 169
 
< 0.1%
0,4341 166
 
< 0.1%
0,4444 164
 
< 0.1%
0,4402 164
 
< 0.1%
0,4436 162
 
< 0.1%
0,4458 158
 
< 0.1%
0,4583 157
 
< 0.1%
0,4585 157
 
< 0.1%
0,4524 156
 
< 0.1%
0,4355 155
 
< 0.1%
Other values (9865) 353864
99.5%

Most occurring characters

ValueCountFrequency (%)
0 474589
22.3%
, 355472
16.7%
4 216028
10.1%
5 173789
 
8.1%
3 166209
 
7.8%
6 134874
 
6.3%
2 133530
 
6.3%
1 126761
 
5.9%
7 124344
 
5.8%
8 117467
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1777360
83.3%
Other Punctuation 355472
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 474589
26.7%
4 216028
12.2%
5 173789
 
9.8%
3 166209
 
9.4%
6 134874
 
7.6%
2 133530
 
7.5%
1 126761
 
7.1%
7 124344
 
7.0%
8 117467
 
6.6%
9 109769
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 355472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2132832
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 474589
22.3%
, 355472
16.7%
4 216028
10.1%
5 173789
 
8.1%
3 166209
 
7.8%
6 134874
 
6.3%
2 133530
 
6.3%
1 126761
 
5.9%
7 124344
 
5.8%
8 117467
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2132832
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 474589
22.3%
, 355472
16.7%
4 216028
10.1%
5 173789
 
8.1%
3 166209
 
7.8%
6 134874
 
6.3%
2 133530
 
6.3%
1 126761
 
5.9%
7 124344
 
5.8%
8 117467
 
5.5%

Gaze point left Y (MCSnorm)
Categorical

HIGH CARDINALITY  MISSING 

Distinct9896
Distinct (%)2.8%
Missing102522
Missing (%)22.4%
Memory size3.5 MiB
0,3913
 
113
0,4479
 
109
0,4411
 
109
0,4867
 
108
0,4189
 
107
Other values (9891)
354926 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters2132832
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique137 ?
Unique (%)< 0.1%

Sample

1st row0,3260
2nd row0,3210
3rd row0,3360
4th row0,3472
5th row0,3401

Common Values

ValueCountFrequency (%)
0,3913 113
 
< 0.1%
0,4479 109
 
< 0.1%
0,4411 109
 
< 0.1%
0,4867 108
 
< 0.1%
0,4189 107
 
< 0.1%
0,3024 106
 
< 0.1%
0,3517 106
 
< 0.1%
0,2885 106
 
< 0.1%
0,4112 106
 
< 0.1%
0,2939 104
 
< 0.1%
Other values (9886) 354398
77.4%
(Missing) 102522
 
22.4%

Length

2023-04-23T20:24:55.259679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,3913 113
 
< 0.1%
0,4479 109
 
< 0.1%
0,4411 109
 
< 0.1%
0,4867 108
 
< 0.1%
0,4189 107
 
< 0.1%
0,3024 106
 
< 0.1%
0,3517 106
 
< 0.1%
0,2885 106
 
< 0.1%
0,4112 106
 
< 0.1%
0,2939 104
 
< 0.1%
Other values (9886) 354398
99.7%

Most occurring characters

ValueCountFrequency (%)
0 481630
22.6%
, 355472
16.7%
4 173620
 
8.1%
3 172313
 
8.1%
2 164979
 
7.7%
5 155444
 
7.3%
1 143982
 
6.8%
6 129862
 
6.1%
7 124694
 
5.8%
8 120469
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1777360
83.3%
Other Punctuation 355472
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 481630
27.1%
4 173620
 
9.8%
3 172313
 
9.7%
2 164979
 
9.3%
5 155444
 
8.7%
1 143982
 
8.1%
6 129862
 
7.3%
7 124694
 
7.0%
8 120469
 
6.8%
9 110367
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 355472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2132832
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 481630
22.6%
, 355472
16.7%
4 173620
 
8.1%
3 172313
 
8.1%
2 164979
 
7.7%
5 155444
 
7.3%
1 143982
 
6.8%
6 129862
 
6.1%
7 124694
 
5.8%
8 120469
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2132832
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 481630
22.6%
, 355472
16.7%
4 173620
 
8.1%
3 172313
 
8.1%
2 164979
 
7.7%
5 155444
 
7.3%
1 143982
 
6.8%
6 129862
 
6.1%
7 124694
 
5.8%
8 120469
 
5.6%

Gaze point right X (MCSnorm)
Categorical

HIGH CARDINALITY  MISSING 

Distinct9596
Distinct (%)3.0%
Missing141115
Missing (%)30.8%
Memory size3.5 MiB
0,4885
 
144
0,4788
 
138
0,4672
 
138
0,4842
 
138
0,4914
 
133
Other values (9591)
316188 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1901274
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique153 ?
Unique (%)< 0.1%

Sample

1st row0,4898
2nd row0,4897
3rd row0,4918
4th row0,4918
5th row0,4937

Common Values

ValueCountFrequency (%)
0,4885 144
 
< 0.1%
0,4788 138
 
< 0.1%
0,4672 138
 
< 0.1%
0,4842 138
 
< 0.1%
0,4914 133
 
< 0.1%
0,5465 131
 
< 0.1%
0,4778 130
 
< 0.1%
0,4728 129
 
< 0.1%
0,4786 129
 
< 0.1%
0,4650 129
 
< 0.1%
Other values (9586) 315540
68.9%
(Missing) 141115
30.8%

Length

2023-04-23T20:24:55.362139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,4885 144
 
< 0.1%
0,4842 138
 
< 0.1%
0,4788 138
 
< 0.1%
0,4672 138
 
< 0.1%
0,4914 133
 
< 0.1%
0,5465 131
 
< 0.1%
0,4778 130
 
< 0.1%
0,4728 129
 
< 0.1%
0,4650 129
 
< 0.1%
0,4908 129
 
< 0.1%
Other values (9586) 315540
99.6%

Most occurring characters

ValueCountFrequency (%)
0 416908
21.9%
, 316879
16.7%
5 175512
9.2%
4 167402
8.8%
6 149961
 
7.9%
3 124655
 
6.6%
7 116897
 
6.1%
2 113542
 
6.0%
8 109155
 
5.7%
1 107912
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1584395
83.3%
Other Punctuation 316879
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 416908
26.3%
5 175512
11.1%
4 167402
10.6%
6 149961
 
9.5%
3 124655
 
7.9%
7 116897
 
7.4%
2 113542
 
7.2%
8 109155
 
6.9%
1 107912
 
6.8%
9 102451
 
6.5%
Other Punctuation
ValueCountFrequency (%)
, 316879
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1901274
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 416908
21.9%
, 316879
16.7%
5 175512
9.2%
4 167402
8.8%
6 149961
 
7.9%
3 124655
 
6.6%
7 116897
 
6.1%
2 113542
 
6.0%
8 109155
 
5.7%
1 107912
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1901274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 416908
21.9%
, 316879
16.7%
5 175512
9.2%
4 167402
8.8%
6 149961
 
7.9%
3 124655
 
6.6%
7 116897
 
6.1%
2 113542
 
6.0%
8 109155
 
5.7%
1 107912
 
5.7%

Gaze point right Y (MCSnorm)
Categorical

HIGH CARDINALITY  MISSING 

Distinct9848
Distinct (%)3.1%
Missing141115
Missing (%)30.8%
Memory size3.5 MiB
0,4116
 
110
0,4252
 
108
0,3321
 
107
0,4240
 
106
0,3971
 
105
Other values (9843)
316343 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1901274
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique188 ?
Unique (%)0.1%

Sample

1st row0,2568
2nd row0,2626
3rd row0,2748
4th row0,2751
5th row0,2736

Common Values

ValueCountFrequency (%)
0,4116 110
 
< 0.1%
0,4252 108
 
< 0.1%
0,3321 107
 
< 0.1%
0,4240 106
 
< 0.1%
0,3971 105
 
< 0.1%
0,3964 104
 
< 0.1%
0,3858 103
 
< 0.1%
0,3278 100
 
< 0.1%
0,4169 100
 
< 0.1%
0,3004 100
 
< 0.1%
Other values (9838) 315836
69.0%
(Missing) 141115
30.8%

Length

2023-04-23T20:24:55.465161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,4116 110
 
< 0.1%
0,4252 108
 
< 0.1%
0,3321 107
 
< 0.1%
0,4240 106
 
< 0.1%
0,3971 105
 
< 0.1%
0,3964 104
 
< 0.1%
0,3858 103
 
< 0.1%
0,4169 100
 
< 0.1%
0,3004 100
 
< 0.1%
0,3278 100
 
< 0.1%
Other values (9838) 315836
99.7%

Most occurring characters

ValueCountFrequency (%)
0 437137
23.0%
, 316879
16.7%
3 158763
 
8.4%
4 151194
 
8.0%
2 145774
 
7.7%
5 135422
 
7.1%
1 129610
 
6.8%
6 117065
 
6.2%
7 108426
 
5.7%
8 103215
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1584395
83.3%
Other Punctuation 316879
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 437137
27.6%
3 158763
 
10.0%
4 151194
 
9.5%
2 145774
 
9.2%
5 135422
 
8.5%
1 129610
 
8.2%
6 117065
 
7.4%
7 108426
 
6.8%
8 103215
 
6.5%
9 97789
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 316879
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1901274
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 437137
23.0%
, 316879
16.7%
3 158763
 
8.4%
4 151194
 
8.0%
2 145774
 
7.7%
5 135422
 
7.1%
1 129610
 
6.8%
6 117065
 
6.2%
7 108426
 
5.7%
8 103215
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1901274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 437137
23.0%
, 316879
16.7%
3 158763
 
8.4%
4 151194
 
8.0%
2 145774
 
7.7%
5 135422
 
7.1%
1 129610
 
6.8%
6 117065
 
6.2%
7 108426
 
5.7%
8 103215
 
5.4%
Distinct7
Distinct (%)< 0.1%
Missing932
Missing (%)0.2%
Memory size3.5 MiB
babelia 6164137243739591
262482 
Photo3
53215 
Photo1
51954 
Photo2
44253 
Photo2 (1)
 
15226
Other values (2)
29932 

Length

Max length24
Median length24
Mean length16.73226
Min length6

Characters and Unicode

Total characters7647680
Distinct characters20
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbabelia 6164137243739591
2nd rowbabelia 6164137243739591
3rd rowbabelia 6164137243739591
4th rowbabelia 6164137243739591
5th rowbabelia 6164137243739591

Common Values

ValueCountFrequency (%)
babelia 6164137243739591 262482
57.3%
Photo3 53215
 
11.6%
Photo1 51954
 
11.3%
Photo2 44253
 
9.7%
Photo2 (1) 15226
 
3.3%
Photo3 (1) 15172
 
3.3%
Photo1 (1) 14760
 
3.2%
(Missing) 932
 
0.2%

Length

2023-04-23T20:24:55.579547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T20:24:55.708540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
babelia 262482
34.3%
6164137243739591 262482
34.3%
photo3 68387
 
8.9%
photo1 66714
 
8.7%
photo2 59479
 
7.8%
1 45158
 
5.9%

Most occurring characters

ValueCountFrequency (%)
1 899318
11.8%
3 855833
11.2%
b 524964
 
6.9%
9 524964
 
6.9%
a 524964
 
6.9%
4 524964
 
6.9%
7 524964
 
6.9%
6 524964
 
6.9%
o 389160
 
5.1%
2 321961
 
4.2%
Other values (10) 2031624
26.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4439450
58.0%
Lowercase Letter 2615694
34.2%
Space Separator 307640
 
4.0%
Uppercase Letter 194580
 
2.5%
Open Punctuation 45158
 
0.6%
Close Punctuation 45158
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 899318
20.3%
3 855833
19.3%
9 524964
11.8%
4 524964
11.8%
7 524964
11.8%
6 524964
11.8%
2 321961
 
7.3%
5 262482
 
5.9%
Lowercase Letter
ValueCountFrequency (%)
b 524964
20.1%
a 524964
20.1%
o 389160
14.9%
i 262482
10.0%
l 262482
10.0%
e 262482
10.0%
h 194580
 
7.4%
t 194580
 
7.4%
Space Separator
ValueCountFrequency (%)
307640
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 194580
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45158
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45158
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4837406
63.3%
Latin 2810274
36.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 899318
18.6%
3 855833
17.7%
9 524964
10.9%
4 524964
10.9%
7 524964
10.9%
6 524964
10.9%
2 321961
 
6.7%
307640
 
6.4%
5 262482
 
5.4%
( 45158
 
0.9%
Latin
ValueCountFrequency (%)
b 524964
18.7%
a 524964
18.7%
o 389160
13.8%
i 262482
9.3%
l 262482
9.3%
e 262482
9.3%
P 194580
 
6.9%
h 194580
 
6.9%
t 194580
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7647680
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 899318
11.8%
3 855833
11.2%
b 524964
 
6.9%
9 524964
 
6.9%
a 524964
 
6.9%
4 524964
 
6.9%
7 524964
 
6.9%
6 524964
 
6.9%
o 389160
 
5.1%
2 321961
 
4.2%
Other values (10) 2031624
26.6%
Distinct4
Distinct (%)< 0.1%
Missing932
Missing (%)0.2%
Memory size3.5 MiB
babelia 6164137243739591.jpg
262482 
Photo3.jpg
68387 
Photo1.jpg
66714 
Photo2.jpg
59479 

Length

Max length28
Median length28
Mean length20.337057
Min length10

Characters and Unicode

Total characters9295296
Distinct characters22
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbabelia 6164137243739591.jpg
2nd rowbabelia 6164137243739591.jpg
3rd rowbabelia 6164137243739591.jpg
4th rowbabelia 6164137243739591.jpg
5th rowbabelia 6164137243739591.jpg

Common Values

ValueCountFrequency (%)
babelia 6164137243739591.jpg 262482
57.3%
Photo3.jpg 68387
 
14.9%
Photo1.jpg 66714
 
14.6%
Photo2.jpg 59479
 
13.0%
(Missing) 932
 
0.2%

Length

2023-04-23T20:24:55.828018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T20:24:55.948633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
babelia 262482
36.5%
6164137243739591.jpg 262482
36.5%
photo3.jpg 68387
 
9.5%
photo1.jpg 66714
 
9.3%
photo2.jpg 59479
 
8.3%

Most occurring characters

ValueCountFrequency (%)
3 855833
 
9.2%
1 854160
 
9.2%
b 524964
 
5.6%
6 524964
 
5.6%
4 524964
 
5.6%
7 524964
 
5.6%
a 524964
 
5.6%
9 524964
 
5.6%
. 457062
 
4.9%
g 457062
 
4.9%
Other values (12) 3521395
37.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4394292
47.3%
Lowercase Letter 3986880
42.9%
Other Punctuation 457062
 
4.9%
Space Separator 262482
 
2.8%
Uppercase Letter 194580
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 524964
13.2%
a 524964
13.2%
g 457062
11.5%
p 457062
11.5%
j 457062
11.5%
o 389160
9.8%
i 262482
6.6%
l 262482
6.6%
e 262482
6.6%
h 194580
 
4.9%
Decimal Number
ValueCountFrequency (%)
3 855833
19.5%
1 854160
19.4%
6 524964
11.9%
4 524964
11.9%
7 524964
11.9%
9 524964
11.9%
2 321961
 
7.3%
5 262482
 
6.0%
Other Punctuation
ValueCountFrequency (%)
. 457062
100.0%
Space Separator
ValueCountFrequency (%)
262482
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 194580
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5113836
55.0%
Latin 4181460
45.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 524964
12.6%
a 524964
12.6%
g 457062
10.9%
p 457062
10.9%
j 457062
10.9%
o 389160
9.3%
i 262482
6.3%
l 262482
6.3%
e 262482
6.3%
P 194580
 
4.7%
Other values (2) 389160
9.3%
Common
ValueCountFrequency (%)
3 855833
16.7%
1 854160
16.7%
6 524964
10.3%
4 524964
10.3%
7 524964
10.3%
9 524964
10.3%
. 457062
8.9%
2 321961
 
6.3%
5 262482
 
5.1%
262482
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9295296
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 855833
 
9.2%
1 854160
 
9.2%
b 524964
 
5.6%
6 524964
 
5.6%
4 524964
 
5.6%
7 524964
 
5.6%
a 524964
 
5.6%
9 524964
 
5.6%
. 457062
 
4.9%
g 457062
 
4.9%
Other values (12) 3521395
37.9%
Distinct4
Distinct (%)< 0.1%
Missing932
Missing (%)0.2%
Memory size3.5 MiB
1662.0
262482 
1920.0
135101 
3415.0
44253 
1080.0
 
15226

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters2742372
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1662.0
2nd row1662.0
3rd row1662.0
4th row1662.0
5th row1662.0

Common Values

ValueCountFrequency (%)
1662.0 262482
57.3%
1920.0 135101
29.5%
3415.0 44253
 
9.7%
1080.0 15226
 
3.3%
(Missing) 932
 
0.2%

Length

2023-04-23T20:24:56.048693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T20:24:56.160724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1662.0 262482
57.4%
1920.0 135101
29.6%
3415.0 44253
 
9.7%
1080.0 15226
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 622615
22.7%
6 524964
19.1%
1 457062
16.7%
. 457062
16.7%
2 397583
14.5%
9 135101
 
4.9%
3 44253
 
1.6%
4 44253
 
1.6%
5 44253
 
1.6%
8 15226
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2285310
83.3%
Other Punctuation 457062
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 622615
27.2%
6 524964
23.0%
1 457062
20.0%
2 397583
17.4%
9 135101
 
5.9%
3 44253
 
1.9%
4 44253
 
1.9%
5 44253
 
1.9%
8 15226
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 457062
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2742372
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 622615
22.7%
6 524964
19.1%
1 457062
16.7%
. 457062
16.7%
2 397583
14.5%
9 135101
 
4.9%
3 44253
 
1.6%
4 44253
 
1.6%
5 44253
 
1.6%
8 15226
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2742372
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 622615
22.7%
6 524964
19.1%
1 457062
16.7%
. 457062
16.7%
2 397583
14.5%
9 135101
 
4.9%
3 44253
 
1.6%
4 44253
 
1.6%
5 44253
 
1.6%
8 15226
 
0.6%
Distinct2
Distinct (%)< 0.1%
Missing932
Missing (%)0.2%
Memory size3.5 MiB
1080.0
412809 
3415.0
44253 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters2742372
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1080.0
2nd row1080.0
3rd row1080.0
4th row1080.0
5th row1080.0

Common Values

ValueCountFrequency (%)
1080.0 412809
90.1%
3415.0 44253
 
9.7%
(Missing) 932
 
0.2%

Length

2023-04-23T20:24:56.259082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T20:24:56.356049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1080.0 412809
90.3%
3415.0 44253
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 1282680
46.8%
1 457062
 
16.7%
. 457062
 
16.7%
8 412809
 
15.1%
3 44253
 
1.6%
4 44253
 
1.6%
5 44253
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2285310
83.3%
Other Punctuation 457062
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1282680
56.1%
1 457062
 
20.0%
8 412809
 
18.1%
3 44253
 
1.9%
4 44253
 
1.9%
5 44253
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 457062
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2742372
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1282680
46.8%
1 457062
 
16.7%
. 457062
 
16.7%
8 412809
 
15.1%
3 44253
 
1.6%
4 44253
 
1.6%
5 44253
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2742372
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1282680
46.8%
1 457062
 
16.7%
. 457062
 
16.7%
8 412809
 
15.1%
3 44253
 
1.6%
4 44253
 
1.6%
5 44253
 
1.6%
Distinct4
Distinct (%)< 0.1%
Missing932
Missing (%)0.2%
Memory size3.5 MiB
129.0
262482 
0.0
135101 
-747.0
44253 
420.0
 
15226

Length

Max length6
Median length5
Mean length4.5056491
Min length3

Characters and Unicode

Total characters2059361
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row129.0
2nd row129.0
3rd row129.0
4th row129.0
5th row129.0

Common Values

ValueCountFrequency (%)
129.0 262482
57.3%
0.0 135101
29.5%
-747.0 44253
 
9.7%
420.0 15226
 
3.3%
(Missing) 932
 
0.2%

Length

2023-04-23T20:24:56.447742image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T20:24:56.568908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
129.0 262482
57.4%
0.0 135101
29.6%
747.0 44253
 
9.7%
420.0 15226
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 607389
29.5%
. 457062
22.2%
2 277708
13.5%
1 262482
12.7%
9 262482
12.7%
7 88506
 
4.3%
4 59479
 
2.9%
- 44253
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1558046
75.7%
Other Punctuation 457062
 
22.2%
Dash Punctuation 44253
 
2.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 607389
39.0%
2 277708
17.8%
1 262482
16.8%
9 262482
16.8%
7 88506
 
5.7%
4 59479
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 457062
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44253
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2059361
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 607389
29.5%
. 457062
22.2%
2 277708
13.5%
1 262482
12.7%
9 262482
12.7%
7 88506
 
4.3%
4 59479
 
2.9%
- 44253
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2059361
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 607389
29.5%
. 457062
22.2%
2 277708
13.5%
1 262482
12.7%
9 262482
12.7%
7 88506
 
4.3%
4 59479
 
2.9%
- 44253
 
2.1%
Distinct2
Distinct (%)< 0.1%
Missing932
Missing (%)0.2%
Memory size3.5 MiB
0.0
412809 
-1168.0
44253 

Length

Max length7
Median length3
Mean length3.3872823
Min length3

Characters and Unicode

Total characters1548198
Distinct characters6
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 412809
90.1%
-1168.0 44253
 
9.7%
(Missing) 932
 
0.2%

Length

2023-04-23T20:24:56.671831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T20:24:56.786777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 412809
90.3%
1168.0 44253
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 869871
56.2%
. 457062
29.5%
1 88506
 
5.7%
- 44253
 
2.9%
6 44253
 
2.9%
8 44253
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1046883
67.6%
Other Punctuation 457062
29.5%
Dash Punctuation 44253
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 869871
83.1%
1 88506
 
8.5%
6 44253
 
4.2%
8 44253
 
4.2%
Other Punctuation
ValueCountFrequency (%)
. 457062
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44253
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1548198
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 869871
56.2%
. 457062
29.5%
1 88506
 
5.7%
- 44253
 
2.9%
6 44253
 
2.9%
8 44253
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1548198
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 869871
56.2%
. 457062
29.5%
1 88506
 
5.7%
- 44253
 
2.9%
6 44253
 
2.9%
8 44253
 
2.9%
Distinct3
Distinct (%)< 0.1%
Missing932
Missing (%)0.2%
Memory size3.5 MiB
640.0
262482 
1920.0
135101 
3415.0
59479 

Length

Max length6
Median length5
Mean length5.425719
Min length5

Characters and Unicode

Total characters2479890
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row640.0
2nd row640.0
3rd row640.0
4th row640.0
5th row640.0

Common Values

ValueCountFrequency (%)
640.0 262482
57.3%
1920.0 135101
29.5%
3415.0 59479
 
13.0%
(Missing) 932
 
0.2%

Length

2023-04-23T20:24:56.875044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T20:24:56.984497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
640.0 262482
57.4%
1920.0 135101
29.6%
3415.0 59479
 
13.0%

Most occurring characters

ValueCountFrequency (%)
0 854645
34.5%
. 457062
18.4%
4 321961
 
13.0%
6 262482
 
10.6%
1 194580
 
7.8%
9 135101
 
5.4%
2 135101
 
5.4%
3 59479
 
2.4%
5 59479
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2022828
81.6%
Other Punctuation 457062
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 854645
42.3%
4 321961
 
15.9%
6 262482
 
13.0%
1 194580
 
9.6%
9 135101
 
6.7%
2 135101
 
6.7%
3 59479
 
2.9%
5 59479
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 457062
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2479890
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 854645
34.5%
. 457062
18.4%
4 321961
 
13.0%
6 262482
 
10.6%
1 194580
 
7.8%
9 135101
 
5.4%
2 135101
 
5.4%
3 59479
 
2.4%
5 59479
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2479890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 854645
34.5%
. 457062
18.4%
4 321961
 
13.0%
6 262482
 
10.6%
1 194580
 
7.8%
9 135101
 
5.4%
2 135101
 
5.4%
3 59479
 
2.4%
5 59479
 
2.4%
Distinct3
Distinct (%)< 0.1%
Missing932
Missing (%)0.2%
Memory size3.5 MiB
416.0
262482 
1080.0
135101 
3415.0
59479 

Length

Max length6
Median length5
Mean length5.425719
Min length5

Characters and Unicode

Total characters2479890
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row416.0
2nd row416.0
3rd row416.0
4th row416.0
5th row416.0

Common Values

ValueCountFrequency (%)
416.0 262482
57.3%
1080.0 135101
29.5%
3415.0 59479
 
13.0%
(Missing) 932
 
0.2%

Length

2023-04-23T20:24:57.077468image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T20:24:57.186192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
416.0 262482
57.4%
1080.0 135101
29.6%
3415.0 59479
 
13.0%

Most occurring characters

ValueCountFrequency (%)
0 727264
29.3%
1 457062
18.4%
. 457062
18.4%
4 321961
13.0%
6 262482
 
10.6%
8 135101
 
5.4%
3 59479
 
2.4%
5 59479
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2022828
81.6%
Other Punctuation 457062
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 727264
36.0%
1 457062
22.6%
4 321961
15.9%
6 262482
 
13.0%
8 135101
 
6.7%
3 59479
 
2.9%
5 59479
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 457062
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2479890
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 727264
29.3%
1 457062
18.4%
. 457062
18.4%
4 321961
13.0%
6 262482
 
10.6%
8 135101
 
5.4%
3 59479
 
2.4%
5 59479
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2479890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 727264
29.3%
1 457062
18.4%
. 457062
18.4%
4 321961
13.0%
6 262482
 
10.6%
8 135101
 
5.4%
3 59479
 
2.4%
5 59479
 
2.4%
Distinct4
Distinct (%)< 0.1%
Missing54
Missing (%)< 0.1%
Memory size3.5 MiB
Fixation
264092 
Saccade
78668 
Unclassified
63683 
EyesNotFound
51497 

Length

Max length12
Median length8
Mean length8.834284
Min length7

Characters and Unicode

Total characters4045572
Distinct characters19
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFixation
2nd rowFixation
3rd rowFixation
4th rowFixation
5th rowFixation

Common Values

ValueCountFrequency (%)
Fixation 264092
57.7%
Saccade 78668
 
17.2%
Unclassified 63683
 
13.9%
EyesNotFound 51497
 
11.2%
(Missing) 54
 
< 0.1%

Length

2023-04-23T20:24:57.281691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T20:24:57.396616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
fixation 264092
57.7%
saccade 78668
 
17.2%
unclassified 63683
 
13.9%
eyesnotfound 51497
 
11.2%

Most occurring characters

ValueCountFrequency (%)
i 655550
16.2%
a 485111
12.0%
n 379272
9.4%
o 367086
9.1%
F 315589
7.8%
t 315589
7.8%
x 264092
6.5%
c 221019
 
5.5%
e 193848
 
4.8%
d 193848
 
4.8%
Other values (9) 654568
16.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3484638
86.1%
Uppercase Letter 560934
 
13.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 655550
18.8%
a 485111
13.9%
n 379272
10.9%
o 367086
10.5%
t 315589
9.1%
x 264092
7.6%
c 221019
 
6.3%
e 193848
 
5.6%
d 193848
 
5.6%
s 178863
 
5.1%
Other values (4) 230360
 
6.6%
Uppercase Letter
ValueCountFrequency (%)
F 315589
56.3%
S 78668
 
14.0%
U 63683
 
11.4%
E 51497
 
9.2%
N 51497
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 4045572
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 655550
16.2%
a 485111
12.0%
n 379272
9.4%
o 367086
9.1%
F 315589
7.8%
t 315589
7.8%
x 264092
6.5%
c 221019
 
5.5%
e 193848
 
4.8%
d 193848
 
4.8%
Other values (9) 654568
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4045572
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 655550
16.2%
a 485111
12.0%
n 379272
9.4%
o 367086
9.1%
F 315589
7.8%
t 315589
7.8%
x 264092
6.5%
c 221019
 
5.5%
e 193848
 
4.8%
d 193848
 
4.8%
Other values (9) 654568
16.2%

Gaze event duration
Real number (ℝ)

Distinct252
Distinct (%)0.1%
Missing54
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean360.97539
Minimum8
Maximum12156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-04-23T20:24:57.517122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8
Q142
median158
Q3342
95-th percentile1267
Maximum12156
Range12148
Interquartile range (IQR)300

Descriptive statistics

Standard deviation906.23003
Coefficient of variation (CV)2.5105037
Kurtosis99.086938
Mean360.97539
Median Absolute Deviation (MAD)133
Skewness8.7247767
Sum1.6530507 × 108
Variance821252.87
MonotonicityNot monotonic
2023-04-23T20:24:57.657773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 55236
 
12.1%
8 27646
 
6.0%
42 15415
 
3.4%
33 14617
 
3.2%
25 13758
 
3.0%
50 13161
 
2.9%
58 11834
 
2.6%
67 10015
 
2.2%
83 7594
 
1.7%
75 7367
 
1.6%
Other values (242) 281297
61.4%
ValueCountFrequency (%)
8 27646
6.0%
16 251
 
0.1%
17 55236
12.1%
24 138
 
< 0.1%
25 13758
 
3.0%
32 12
 
< 0.1%
33 14617
 
3.2%
41 80
 
< 0.1%
42 15415
 
3.4%
49 6
 
< 0.1%
ValueCountFrequency (%)
12156 1516
0.3%
6567 791
0.2%
4919 592
 
0.1%
4893 589
 
0.1%
4311 520
 
0.1%
3476 434
 
0.1%
3425 411
 
0.1%
3297 397
 
0.1%
3225 387
 
0.1%
3167 380
 
0.1%

Eye movement type index
Real number (ℝ)

Distinct2656
Distinct (%)0.6%
Missing54
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean296.73321
Minimum1
Maximum2656
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-04-23T20:24:57.797985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25
Q176
median151
Q3362
95-th percentile1180
Maximum2656
Range2655
Interquartile range (IQR)286

Descriptive statistics

Standard deviation385.95432
Coefficient of variation (CV)1.3006779
Kurtosis7.9733031
Mean296.73321
Median Absolute Deviation (MAD)97
Skewness2.6705572
Sum1.35886 × 108
Variance148960.74
MonotonicityNot monotonic
2023-04-23T20:24:57.934614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 3690
 
0.8%
21 2502
 
0.5%
93 2453
 
0.5%
38 2398
 
0.5%
25 2305
 
0.5%
32 2183
 
0.5%
37 2157
 
0.5%
165 2104
 
0.5%
94 2088
 
0.5%
23 2085
 
0.5%
Other values (2646) 433975
94.8%
ValueCountFrequency (%)
1 17
 
< 0.1%
2 129
 
< 0.1%
3 109
 
< 0.1%
4 110
 
< 0.1%
5 123
 
< 0.1%
6 290
0.1%
7 194
 
< 0.1%
8 370
0.1%
9 452
0.1%
10 556
0.1%
ValueCountFrequency (%)
2656 1
 
< 0.1%
2655 1
 
< 0.1%
2654 1
 
< 0.1%
2653 1
 
< 0.1%
2652 1
 
< 0.1%
2651 1
 
< 0.1%
2650 1
 
< 0.1%
2649 3
 
< 0.1%
2648 16
< 0.1%
2647 3
 
< 0.1%

Fixation point X
Real number (ℝ)

Distinct1470
Distinct (%)0.6%
Missing193902
Missing (%)42.3%
Infinite0
Infinite (%)0.0%
Mean945.64047
Minimum-23
Maximum2193
Zeros92
Zeros (%)< 0.1%
Negative33
Negative (%)< 0.1%
Memory size3.5 MiB
2023-04-23T20:24:58.068051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-23
5-th percentile356
Q1774
median949
Q31114
95-th percentile1518
Maximum2193
Range2216
Interquartile range (IQR)340

Descriptive statistics

Standard deviation328.78792
Coefficient of variation (CV)0.34768808
Kurtosis0.6709952
Mean945.64047
Median Absolute Deviation (MAD)172
Skewness0.10358314
Sum2.4973608 × 108
Variance108101.5
MonotonicityNot monotonic
2023-04-23T20:24:58.195152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
892 1068
 
0.2%
1005 998
 
0.2%
1020 965
 
0.2%
885 946
 
0.2%
970 940
 
0.2%
1009 937
 
0.2%
992 934
 
0.2%
974 911
 
0.2%
911 893
 
0.2%
1077 856
 
0.2%
Other values (1460) 254644
55.6%
(Missing) 193902
42.3%
ValueCountFrequency (%)
-23 33
 
< 0.1%
0 92
< 0.1%
5 36
 
< 0.1%
8 84
< 0.1%
16 36
 
< 0.1%
33 152
< 0.1%
38 30
 
< 0.1%
44 52
 
< 0.1%
47 72
< 0.1%
55 50
 
< 0.1%
ValueCountFrequency (%)
2193 16
 
< 0.1%
2149 19
 
< 0.1%
2134 24
 
< 0.1%
2133 25
< 0.1%
2121 54
< 0.1%
2110 52
< 0.1%
2107 8
 
< 0.1%
2100 31
< 0.1%
2096 62
< 0.1%
2093 33
< 0.1%

Fixation point Y
Real number (ℝ)

Distinct1158
Distinct (%)0.4%
Missing193902
Missing (%)42.3%
Infinite0
Infinite (%)0.0%
Mean399.99384
Minimum-205
Maximum1270
Zeros248
Zeros (%)0.1%
Negative12343
Negative (%)2.7%
Memory size3.5 MiB
2023-04-23T20:24:58.329982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-205
5-th percentile6
Q1233
median371
Q3563
95-th percentile870
Maximum1270
Range1475
Interquartile range (IQR)330

Descriptive statistics

Standard deviation252.9692
Coefficient of variation (CV)0.63243273
Kurtosis-0.12016988
Mean399.99384
Median Absolute Deviation (MAD)161
Skewness0.38905889
Sum1.0563517 × 108
Variance63993.415
MonotonicityNot monotonic
2023-04-23T20:24:58.464467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
444 1297
 
0.3%
371 1238
 
0.3%
226 1160
 
0.3%
278 998
 
0.2%
291 948
 
0.2%
236 930
 
0.2%
324 879
 
0.2%
265 877
 
0.2%
315 871
 
0.2%
301 848
 
0.2%
Other values (1148) 254046
55.5%
(Missing) 193902
42.3%
ValueCountFrequency (%)
-205 16
 
< 0.1%
-196 64
< 0.1%
-181 66
< 0.1%
-177 111
< 0.1%
-173 16
 
< 0.1%
-171 35
 
< 0.1%
-167 44
 
< 0.1%
-166 26
 
< 0.1%
-164 10
 
< 0.1%
-161 90
< 0.1%
ValueCountFrequency (%)
1270 15
 
< 0.1%
1241 20
 
< 0.1%
1239 27
 
< 0.1%
1236 23
 
< 0.1%
1233 8
 
< 0.1%
1215 21
 
< 0.1%
1212 13
 
< 0.1%
1211 81
< 0.1%
1210 27
 
< 0.1%
1205 84
< 0.1%

Fixation point X (MCSnorm)
Categorical

HIGH CARDINALITY  MISSING 

Distinct3601
Distinct (%)1.4%
Missing208868
Missing (%)45.6%
Memory size3.5 MiB
0,5085
 
558
0,4130
 
539
0,4500
 
489
0,4708
 
467
0,5295
 
451
Other values (3596)
246622 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1494756
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0,4688
2nd row0,4688
3rd row0,4688
4th row0,4688
5th row0,4688

Common Values

ValueCountFrequency (%)
0,5085 558
 
0.1%
0,4130 539
 
0.1%
0,4500 489
 
0.1%
0,4708 467
 
0.1%
0,5295 451
 
0.1%
0,5353 449
 
0.1%
0,5418 440
 
0.1%
0,4815 435
 
0.1%
0,4400 411
 
0.1%
0,5265 408
 
0.1%
Other values (3591) 244479
53.4%
(Missing) 208868
45.6%

Length

2023-04-23T20:24:58.586880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,5085 558
 
0.2%
0,4130 539
 
0.2%
0,4500 489
 
0.2%
0,4708 467
 
0.2%
0,5295 451
 
0.2%
0,5353 449
 
0.2%
0,5418 440
 
0.2%
0,4815 435
 
0.2%
0,4400 411
 
0.2%
0,5265 408
 
0.2%
Other values (3591) 244479
98.1%

Most occurring characters

ValueCountFrequency (%)
0 330592
22.1%
, 249126
16.7%
4 144119
9.6%
5 143905
9.6%
3 105158
 
7.0%
6 97229
 
6.5%
2 88157
 
5.9%
1 87965
 
5.9%
8 84754
 
5.7%
7 84578
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1245630
83.3%
Other Punctuation 249126
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 330592
26.5%
4 144119
11.6%
5 143905
11.6%
3 105158
 
8.4%
6 97229
 
7.8%
2 88157
 
7.1%
1 87965
 
7.1%
8 84754
 
6.8%
7 84578
 
6.8%
9 79173
 
6.4%
Other Punctuation
ValueCountFrequency (%)
, 249126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1494756
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 330592
22.1%
, 249126
16.7%
4 144119
9.6%
5 143905
9.6%
3 105158
 
7.0%
6 97229
 
6.5%
2 88157
 
5.9%
1 87965
 
5.9%
8 84754
 
5.7%
7 84578
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1494756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 330592
22.1%
, 249126
16.7%
4 144119
9.6%
5 143905
9.6%
3 105158
 
7.0%
6 97229
 
6.5%
2 88157
 
5.9%
1 87965
 
5.9%
8 84754
 
5.7%
7 84578
 
5.7%

Fixation point Y (MCSnorm)
Categorical

HIGH CARDINALITY  MISSING 

Distinct3830
Distinct (%)1.5%
Missing208868
Missing (%)45.6%
Memory size3.5 MiB
0,2454
 
508
0,3493
 
479
0,5630
 
459
0,0925
 
456
0,2578
 
440
Other values (3825)
246784 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1494756
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0,2924
2nd row0,2924
3rd row0,2924
4th row0,2924
5th row0,2924

Common Values

ValueCountFrequency (%)
0,2454 508
 
0.1%
0,3493 479
 
0.1%
0,5630 459
 
0.1%
0,0925 456
 
0.1%
0,2578 440
 
0.1%
0,2798 432
 
0.1%
0,3433 393
 
0.1%
0,2887 392
 
0.1%
0,5210 381
 
0.1%
0,3201 380
 
0.1%
Other values (3820) 244806
53.5%
(Missing) 208868
45.6%

Length

2023-04-23T20:24:58.682984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,2454 508
 
0.2%
0,3493 479
 
0.2%
0,5630 459
 
0.2%
0,0925 456
 
0.2%
0,2578 440
 
0.2%
0,2798 432
 
0.2%
0,3433 393
 
0.2%
0,2887 392
 
0.2%
0,5210 381
 
0.2%
0,3201 380
 
0.2%
Other values (3820) 244806
98.3%

Most occurring characters

ValueCountFrequency (%)
0 343258
23.0%
, 249126
16.7%
3 126306
 
8.4%
4 119475
 
8.0%
2 116753
 
7.8%
5 108516
 
7.3%
1 101570
 
6.8%
6 87946
 
5.9%
7 85879
 
5.7%
8 80627
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1245630
83.3%
Other Punctuation 249126
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 343258
27.6%
3 126306
 
10.1%
4 119475
 
9.6%
2 116753
 
9.4%
5 108516
 
8.7%
1 101570
 
8.2%
6 87946
 
7.1%
7 85879
 
6.9%
8 80627
 
6.5%
9 75300
 
6.0%
Other Punctuation
ValueCountFrequency (%)
, 249126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1494756
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 343258
23.0%
, 249126
16.7%
3 126306
 
8.4%
4 119475
 
8.0%
2 116753
 
7.8%
5 108516
 
7.3%
1 101570
 
6.8%
6 87946
 
5.9%
7 85879
 
5.7%
8 80627
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1494756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 343258
23.0%
, 249126
16.7%
3 126306
 
8.4%
4 119475
 
8.0%
2 116753
 
7.8%
5 108516
 
7.3%
1 101570
 
6.8%
6 87946
 
5.9%
7 85879
 
5.7%
8 80627
 
5.4%

Mouse position X
Real number (ℝ)

Distinct1214
Distinct (%)20.4%
Missing452050
Missing (%)98.7%
Infinite0
Infinite (%)0.0%
Mean-422.0318
Minimum-1646
Maximum1081
Zeros0
Zeros (%)0.0%
Negative5373
Negative (%)1.2%
Memory size3.5 MiB
2023-04-23T20:24:58.796548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1646
5-th percentile-1262
Q1-510
median-417
Q3-281
95-th percentile266
Maximum1081
Range2727
Interquartile range (IQR)229

Descriptive statistics

Standard deviation411.6426
Coefficient of variation (CV)-0.9753829
Kurtosis2.5420149
Mean-422.0318
Median Absolute Deviation (MAD)108
Skewness0.12854962
Sum-2508557
Variance169449.63
MonotonicityNot monotonic
2023-04-23T20:24:58.934956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-423 80
 
< 0.1%
-408 74
 
< 0.1%
-432 59
 
< 0.1%
-415 55
 
< 0.1%
-433 45
 
< 0.1%
-454 40
 
< 0.1%
-412 39
 
< 0.1%
-397 39
 
< 0.1%
-428 39
 
< 0.1%
-414 38
 
< 0.1%
Other values (1204) 5436
 
1.2%
(Missing) 452050
98.7%
ValueCountFrequency (%)
-1646 1
< 0.1%
-1644 1
< 0.1%
-1636 1
< 0.1%
-1634 1
< 0.1%
-1632 1
< 0.1%
-1631 1
< 0.1%
-1628 2
< 0.1%
-1612 1
< 0.1%
-1611 1
< 0.1%
-1603 1
< 0.1%
ValueCountFrequency (%)
1081 3
< 0.1%
1080 3
< 0.1%
1079 3
< 0.1%
1075 3
< 0.1%
1065 3
< 0.1%
1060 3
< 0.1%
1037 3
< 0.1%
1030 3
< 0.1%
998 3
< 0.1%
984 3
< 0.1%

Mouse position Y
Real number (ℝ)

Distinct979
Distinct (%)16.5%
Missing452050
Missing (%)98.7%
Infinite0
Infinite (%)0.0%
Mean791.62685
Minimum-77
Maximum1253
Zeros3
Zeros (%)< 0.1%
Negative12
Negative (%)< 0.1%
Memory size3.5 MiB
2023-04-23T20:24:59.064348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-77
5-th percentile268
Q1634.75
median875
Q3981
95-th percentile1091.85
Maximum1253
Range1330
Interquartile range (IQR)346.25

Descriptive statistics

Standard deviation255.19841
Coefficient of variation (CV)0.3223721
Kurtosis0.56931597
Mean791.62685
Median Absolute Deviation (MAD)131
Skewness-1.0398924
Sum4705430
Variance65126.227
MonotonicityNot monotonic
2023-04-23T20:24:59.203274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
981 98
 
< 0.1%
983 93
 
< 0.1%
982 84
 
< 0.1%
980 74
 
< 0.1%
986 73
 
< 0.1%
979 62
 
< 0.1%
984 61
 
< 0.1%
976 51
 
< 0.1%
985 43
 
< 0.1%
960 39
 
< 0.1%
Other values (969) 5266
 
1.1%
(Missing) 452050
98.7%
ValueCountFrequency (%)
-77 1
< 0.1%
-58 1
< 0.1%
-53 1
< 0.1%
-36 1
< 0.1%
-25 1
< 0.1%
-6 1
< 0.1%
-4 1
< 0.1%
-3 2
< 0.1%
-2 1
< 0.1%
-1 2
< 0.1%
ValueCountFrequency (%)
1253 1
< 0.1%
1251 1
< 0.1%
1236 1
< 0.1%
1235 1
< 0.1%
1225 1
< 0.1%
1223 2
< 0.1%
1214 1
< 0.1%
1212 2
< 0.1%
1210 1
< 0.1%
1209 2
< 0.1%

Interactions

2023-04-23T20:24:23.554724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:35.876622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:39.204200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:42.338661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:45.560934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:48.548391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:51.840904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:54.755419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:57.921115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:00.847668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:03.753249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:06.741475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:09.704797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:12.811001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:15.894089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:18.831706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:21.654250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:23.676451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:36.098331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:39.426970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:42.551353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:45.754656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:48.759598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:52.032842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:54.956862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:58.111120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:01.037786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:03.934046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:06.933619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:09.911164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:13.014780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:16.068258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:19.005607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:21.777087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:23.803007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:36.294577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:39.618081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:42.753096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:45.943351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:48.961730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:52.218953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:55.141341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:58.293869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:01.218115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:04.109275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:07.117844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:10.114257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:13.213372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:16.234374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:19.172395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:21.904164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:23.943216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:36.654057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:39.835283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:42.965944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:46.145741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:49.179796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:52.422503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:55.344800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:58.499275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:01.421097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:04.301723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:07.318533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:10.332060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:13.430694image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:16.408681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:19.350317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:22.035732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:24.057908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:36.846927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:40.035214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:43.164837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:46.329859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:49.378642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:52.609072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:55.587240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:58.696692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:01.596605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:04.467845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:07.492244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:10.523419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:13.626456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:16.555710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:19.511800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:22.145130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:24.146111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:37.057493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:40.247379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:43.373373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:46.527466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:49.584999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:52.816160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:55.857315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:58.921059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:01.793808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:04.656588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:07.692334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:10.730203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:13.835109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:16.729108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:19.686634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:22.239561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:24.237142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:37.250351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:40.442069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:43.565425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:46.714040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:49.774573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:52.995115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:56.072305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:59.100600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:01.975250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:05.013181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:07.872284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:10.919029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:14.028236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:16.880943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:19.842707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:22.331738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:24.339794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:37.436181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:40.625387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:43.761830image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:46.886161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:49.958299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:53.171162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:56.265248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:59.277880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:02.153576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:05.190052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:08.078264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:11.102734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:14.210329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:17.044540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:20.030181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:22.425410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:24.432777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:37.630317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:40.806301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:43.948715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:47.060785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:50.320421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:53.347970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:56.448464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:59.456359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:02.329784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:05.360092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:08.254618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:11.286851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:14.389702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:17.194845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:20.189925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:22.517266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:24.523809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:37.807993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:40.977616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:44.132218image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:47.230999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:50.497823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:53.514207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:56.641668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:59.623566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:02.493469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:05.543355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:08.446375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:11.465868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:14.563377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:17.360164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:20.366996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:22.607935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:24.614214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:37.990405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:41.151403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:44.328320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:47.399683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:50.680735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:53.689281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:56.822258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:59.791739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:02.673268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:05.711911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:08.627007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:11.642464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:14.735423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:17.513319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:20.519461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:22.698644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:24.710362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:38.213875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:41.358805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:44.555367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:47.600484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:50.896822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:53.880820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:57.023611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:59.987309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:02.877521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:05.895445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:08.815428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:11.847040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:14.940553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:17.694940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:20.695158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:22.791242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:24.831918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:38.426694image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:41.568521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:44.770838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:47.802233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:51.104230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:54.075384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:57.219419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:00.179891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:03.075023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:06.080728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:09.006490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:12.048607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:15.143007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:17.889364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:20.866667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:22.918838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:24.944326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:38.590664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:41.742655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:44.942741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:47.961588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:51.268988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:54.229153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:57.379541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:00.336686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:03.229487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:06.234022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:09.164133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:12.214159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:15.301000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:18.057859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:21.082882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:23.040358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:25.072286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:38.762153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:41.900219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:45.115905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:48.130886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:51.430975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:54.378680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:57.556072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:00.485121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:03.390407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:06.381700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:09.319647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:12.373141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:15.459816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:18.209553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:21.268216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:23.163229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:25.199422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:38.883933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:42.016282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:45.243885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:48.249717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:51.557653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:54.501664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:57.673483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:00.600303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:03.509920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:06.494173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:09.440515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:12.492383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:15.583556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:18.341953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:21.408487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:23.281913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:25.312165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:39.001577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:42.130270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:45.357398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:48.352914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:51.665648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:54.585036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:23:57.760432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:00.686350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:03.595534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:06.583252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:09.527264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:12.603202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:15.694577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:18.460115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:21.528543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-23T20:24:23.403344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Missing values

2023-04-23T20:24:27.873274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-23T20:24:31.557095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-23T20:24:42.351035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0Recording timestampComputer timestampSensorProject nameExport dateParticipant nameRecording nameRecording dateRecording date UTCRecording start timeRecording start time UTCRecording durationTimeline nameRecording Fixation filter nameRecording software versionRecording resolution heightRecording resolution widthRecording monitor latencyEyetracker timestampEventEvent valueGaze point XGaze point YGaze point left XGaze point left YGaze point right XGaze point right YGaze direction left XGaze direction left YGaze direction left ZGaze direction right XGaze direction right YGaze direction right ZPupil diameter leftPupil diameter rightValidity leftValidity rightEye position left X (DACSmm)Eye position left Y (DACSmm)Eye position left Z (DACSmm)Eye position right X (DACSmm)Eye position right Y (DACSmm)Eye position right Z (DACSmm)Gaze point left X (DACSmm)Gaze point left Y (DACSmm)Gaze point right X (DACSmm)Gaze point right Y (DACSmm)Gaze point X (MCSnorm)Gaze point Y (MCSnorm)Gaze point left X (MCSnorm)Gaze point left Y (MCSnorm)Gaze point right X (MCSnorm)Gaze point right Y (MCSnorm)Presented Stimulus namePresented Media namePresented Media widthPresented Media heightPresented Media position X (DACSpx)Presented Media position Y (DACSpx)Original Media widthOriginal Media heightEye movement typeGaze event durationEye movement type indexFixation point XFixation point YFixation point X (MCSnorm)Fixation point Y (MCSnorm)Mouse position XMouse position Y
0143886735083515109903817NaNControl group experiment30.09.2020Participant0002Recording230.09.202030.09.202014:53:16.98012:53:16.98014124Timeline1Tobii I-VT (Fixation)1.145.281801080192010,00NaNEye tracker Calibration endNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFixation525.020.0908.0316.00,46880,2924NaNNaN
1143896735083515109903817NaNControl group experiment30.09.2020Participant0002Recording230.09.202030.09.202014:53:16.98012:53:16.98014124Timeline1Tobii I-VT (Fixation)1.145.281801080192010,00NaNImageStimulusStartbabelia 6164137243739591NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFixation525.020.0908.0316.00,46880,2924NaNNaN
2143906735678515109904412Eye TrackerControl group experiment30.09.2020Participant0002Recording230.09.202030.09.202014:53:16.98012:53:16.98014124Timeline1Tobii I-VT (Fixation)1.145.281801080192010,00603994977.0NaNNaN898.0315.0853.0352.0943.0277.00,046620,07270-0,99626-0,024090,04899-0,99851NaNNaNValidValid206,353,7585,0272,846,7595,5233,796,3258,575,90,46260,29140,43540,32600,48980,2568babelia 6164137243739591babelia 6164137243739591.jpg1662.01080.0129.00.0640.0416.0Fixation525.020.0908.0316.00,46880,2924NaNNaN
3143916744010515109912744Eye TrackerControl group experiment30.09.2020Participant0002Recording230.09.202030.09.202014:53:16.98012:53:16.98014124Timeline1Tobii I-VT (Fixation)1.145.281801080192010,00604003309.0NaNNaN899.0315.0855.0347.0943.0284.00,047690,07029-0,99639-0,024190,05181-0,998363,003,16ValidValid206,353,6585,1272,846,7595,4234,394,9258,477,60,46330,29180,43680,32100,48970,2626babelia 6164137243739591babelia 6164137243739591.jpg1662.01080.0129.00.0640.0416.0Fixation525.020.0908.0316.00,46880,2924NaNNaN
4143926752340515109921074Eye TrackerControl group experiment30.09.2020Participant0002Recording230.09.202030.09.202014:53:16.98012:53:16.98014124Timeline1Tobii I-VT (Fixation)1.145.281801080192010,00604011639.0NaNNaN906.0330.0866.0363.0946.0297.00,052950,07778-0,99556-0,022580,05768-0,99808NaNNaNValidValid206,353,6585,1272,946,8595,3237,499,3259,481,20,46770,30540,44360,33600,49180,2748babelia 6164137243739591babelia 6164137243739591.jpg1662.01080.0129.00.0640.0416.0Fixation525.020.0908.0316.00,46880,2924NaNNaN
5143936760678515109929412Eye TrackerControl group experiment30.09.2020Participant0002Recording230.09.202030.09.202014:53:16.98012:53:16.98014124Timeline1Tobii I-VT (Fixation)1.145.281801080192010,00604019977.0NaNNaN902.0336.0858.0375.0946.0297.00,049210,08342-0,99530-0,022480,05779-0,99808NaNNaNValidValid206,253,6585,3272,846,9595,2235,2102,6259,481,30,46520,31120,43870,34720,49180,2751babelia 6164137243739591babelia 6164137243739591.jpg1662.01080.0129.00.0640.0416.0Fixation525.020.0908.0316.00,46880,2924NaNNaN
6143946769008515109937742Eye TrackerControl group experiment30.09.2020Participant0002Recording230.09.202030.09.202014:53:16.98012:53:16.98014124Timeline1Tobii I-VT (Fixation)1.145.281801080192010,00604028306.0NaNNaN903.0331.0856.0367.0949.0295.00,048380,07999-0,99562-0,021060,05694-0,998162,883,16ValidValid206,253,5585,5272,846,9595,1234,7100,5260,280,90,46560,30690,43760,34010,49370,2736babelia 6164137243739591babelia 6164137243739591.jpg1662.01080.0129.00.0640.0416.0Fixation525.020.0908.0316.00,46880,2924NaNNaN
7143956777340515109946074Eye TrackerControl group experiment30.09.2020Participant0002Recording230.09.202030.09.202014:53:16.98012:53:16.98014124Timeline1Tobii I-VT (Fixation)1.145.281801080192010,00604036638.0NaNNaN905.0308.0865.0328.0945.0288.00,052440,06155-0,99673-0,023250,05311-0,99832NaNNaNValidValid206,253,5585,6272,847,0594,9237,089,6258,978,70,46680,28480,44270,30320,49080,2663babelia 6164137243739591babelia 6164137243739591.jpg1662.01080.0129.00.0640.0416.0Fixation525.020.0908.0316.00,46880,2924NaNNaN
8143966785675515109954409Eye TrackerControl group experiment30.09.2020Participant0002Recording230.09.202030.09.202014:53:16.98012:53:16.98014124Timeline1Tobii I-VT (Fixation)1.145.281801080192010,00604044973.0NaNNaN905.0307.0863.0329.0947.0284.00,051630,06251-0,99671-0,022000,05152-0,99843NaNNaNValidValid206,153,4585,8272,747,1594,8236,590,2259,677,80,46690,28420,44150,30500,49230,2633babelia 6164137243739591babelia 6164137243739591.jpg1662.01080.0129.00.0640.0416.0Fixation525.020.0908.0316.00,46880,2924NaNNaN
9143976794004515109962738Eye TrackerControl group experiment30.09.2020Participant0002Recording230.09.202030.09.202014:53:16.98012:53:16.98014124Timeline1Tobii I-VT (Fixation)1.145.281801080192010,00604053301.0NaNNaN907.0310.0865.0340.0950.0280.00,052410,06764-0,99633-0,020660,04938-0,998572,963,11ValidValid206,153,3586,1272,747,2594,7236,993,1260,476,60,46830,28710,44260,31500,49400,2592babelia 6164137243739591babelia 6164137243739591.jpg1662.01080.0129.00.0640.0416.0Fixation525.020.0908.0316.00,46880,2924NaNNaN
Unnamed: 0Recording timestampComputer timestampSensorProject nameExport dateParticipant nameRecording nameRecording dateRecording date UTCRecording start timeRecording start time UTCRecording durationTimeline nameRecording Fixation filter nameRecording software versionRecording resolution heightRecording resolution widthRecording monitor latencyEyetracker timestampEventEvent valueGaze point XGaze point YGaze point left XGaze point left YGaze point right XGaze point right YGaze direction left XGaze direction left YGaze direction left ZGaze direction right XGaze direction right YGaze direction right ZPupil diameter leftPupil diameter rightValidity leftValidity rightEye position left X (DACSmm)Eye position left Y (DACSmm)Eye position left Z (DACSmm)Eye position right X (DACSmm)Eye position right Y (DACSmm)Eye position right Z (DACSmm)Gaze point left X (DACSmm)Gaze point left Y (DACSmm)Gaze point right X (DACSmm)Gaze point right Y (DACSmm)Gaze point X (MCSnorm)Gaze point Y (MCSnorm)Gaze point left X (MCSnorm)Gaze point left Y (MCSnorm)Gaze point right X (MCSnorm)Gaze point right Y (MCSnorm)Presented Stimulus namePresented Media namePresented Media widthPresented Media heightPresented Media position X (DACSpx)Presented Media position Y (DACSpx)Original Media widthOriginal Media heightEye movement typeGaze event durationEye movement type indexFixation point XFixation point YFixation point X (MCSnorm)Fixation point Y (MCSnorm)Mouse position XMouse position Y
45798423420636235981400421335364Eye TrackerParticipant005817.09.2021Participant0058Recording317.09.202117.09.202114:47:24.26712:47:24.26763857Timeline1Tobii I-VT (Fixation)1.145.281801080192010,00649929088.0NaNNaN1035.0259.01037.0268.01032.0249.00,09940-0,05456-0,99355-0,01570-0,06040-0,99805NaNNaNValidValid232,7101,7513,9291,099,2512,9284,173,4283,068,20,54490,23960,54620,24850,54360,2306babelia 6164137243739591babelia 6164137243739591.jpg1662.01080.0129.00.0640.0416.0Fixation200.0130.01035.0254.00,54520,2348NaNNaN
45798523421636319301400421343696Eye TrackerParticipant005817.09.2021Participant0058Recording317.09.202117.09.202114:47:24.26712:47:24.26763857Timeline1Tobii I-VT (Fixation)1.145.281801080192010,00649937419.0NaNNaN1040.0258.01045.0266.01035.0249.00,10373-0,05556-0,99305-0,01452-0,06041-0,998074,114,10ValidValid232,7101,5514,0291,099,3512,5286,472,7283,668,30,54810,23850,55120,24610,54500,2310babelia 6164137243739591babelia 6164137243739591.jpg1662.01080.0129.00.0640.0416.0Fixation200.0130.01035.0254.00,54520,2348NaNNaN
45798623422636402741400421352040Eye TrackerParticipant005817.09.2021Participant0058Recording317.09.202117.09.202114:47:24.26712:47:24.26763857Timeline1Tobii I-VT (Fixation)1.145.281801080192010,00649945763.0NaNNaN1043.0265.01047.0272.01040.0258.00,10442-0,05244-0,99315-0,01159-0,05670-0,99832NaNNaNValidValid232,8101,5514,2291,099,6512,0286,874,4285,170,50,55010,24510,55210,25160,54820,2385babelia 6164137243739591babelia 6164137243739591.jpg1662.01080.0129.00.0640.0416.0Fixation200.0130.01035.0254.00,54520,2348NaNNaN
45798723423636469641400421358730NaNParticipant005817.09.2021Participant0058Recording317.09.202117.09.202114:47:24.26712:47:24.26763857Timeline1Tobii I-VT (Fixation)1.145.281801080192010,00NaNImageStimulusEndbabelia 6164137243739591NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFixation200.0130.01035.0254.0NaNNaNNaNNaN
45798823424636486461400421360412Eye TrackerParticipant005817.09.2021Participant0058Recording317.09.202117.09.202114:47:24.26712:47:24.26763857Timeline1Tobii I-VT (Fixation)1.145.281801080192010,00649954135.0NaNNaN1047.0260.01054.0264.01040.0256.00,10860-0,05663-0,99247-0,01162-0,05781-0,99826NaNNaNValidValid232,7101,5514,3290,999,7511,7289,072,1285,070,1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFixation200.0130.01035.0254.0NaNNaNNaNNaN
45798923425636569741400421368740Eye TrackerParticipant005817.09.2021Participant0058Recording317.09.202117.09.202114:47:24.26712:47:24.26763857Timeline1Tobii I-VT (Fixation)1.145.281801080192010,00649962462.0NaNNaN1042.0262.01046.0266.01039.0258.00,10417-0,05512-0,99303-0,01223-0,05688-0,998313,874,12ValidValid232,7101,3514,5291,099,8511,4286,772,8284,770,6NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFixation200.0130.01035.0254.0NaNNaNNaNNaN
45799023426636652741400421377040Eye TrackerParticipant005817.09.2021Participant0058Recording317.09.202117.09.202114:47:24.26712:47:24.26763857Timeline1Tobii I-VT (Fixation)1.145.281801080192010,00649970762.0NaNNaN1040.0260.01043.0261.01037.0259.00,10268-0,05754-0,99305-0,01306-0,05699-0,99829NaNNaNValidValid232,7101,4514,6290,999,9511,1285,971,6284,270,8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFixation200.0130.01035.0254.0NaNNaNNaNNaN
45799123427636735991400421385365Eye TrackerParticipant005817.09.2021Participant0058Recording317.09.202117.09.202114:47:24.26712:47:24.26763857Timeline1Tobii I-VT (Fixation)1.145.281801080192010,00649979087.0NaNNaN1029.0258.01026.0259.01032.0257.00,09371-0,05878-0,99386-0,01552-0,05821-0,99818NaNNaNValidValid232,7101,4514,7290,9100,0510,9281,270,9282,970,2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFixation200.0130.01035.0254.0NaNNaNNaNNaN
45799223428636819781400421393744Eye TrackerParticipant005817.09.2021Participant0058Recording317.09.202117.09.202114:47:24.26712:47:24.26763857Timeline1Tobii I-VT (Fixation)1.145.281801080192010,00649987466.0NaNNaN1035.0252.01032.0250.01038.0254.00,09674-0,06314-0,99331-0,01245-0,05956-0,998154,134,11ValidValid232,7101,2514,8290,9100,0510,8282,868,5284,569,5NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFixation200.0130.01035.0254.0NaNNaNNaNNaN
45799323429636903671400421402133Eye TrackerParticipant005817.09.2021Participant0058Recording317.09.202117.09.202114:47:24.26712:47:24.26763857Timeline1Tobii I-VT (Fixation)1.145.281801080192010,00649995854.0NaNNaN1034.0267.01035.0267.01034.0267.00,09816-0,05469-0,99367-0,01491-0,05266-0,99850NaNNaNValidValid232,7101,3514,8290,9100,1510,7283,672,9283,373,2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFixation200.0130.01035.0254.0NaNNaNNaNNaN